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In the current working directory, `TASK.md` describes a bounded scientific task and the directory `out/` contains the deliverables submitted by another assistant (you have not seen how they were produced and you must not look for their working transcript). There is no network access. Use `~/wp29/venv/bin/python` if you need to re-run or test code. Do not modify any file under `out/` except that you must create `out/review/review.json`.\n\nReview the deliverables against `TASK.md` only. Specifically check:\n1. Does `out/results.json` contain every required key with legitimately computed values? Re-run `out/analysis.py` in a scratch copy if needed and compare.\n2. Are the information-availability rules of `TASK.md` respected by the code (for example no use of future information, no use of forbidden inputs)? Read the code to verify; do not trust the report.\n3. Are the central claims in `out/report.md` and `out/claims.json` supported by the numbers in `out/results.json`, with appropriate uncertainty and caveats? Flag any claim that is stronger than the evidence.\n4. Are counts, exclusions, units, signs and magnitudes internally consistent? Are baseline comparisons present when the task requires them?\n5. Are limitations stated honestly? Is anything declared complete that is not?\n\nYou have no reference answers; do not guess what the \"right\" number is. Report only issues you can justify from the files. Write `out/review/review.json` as {\"verdict\": \"accept\"|\"revise\", \"issues\": [{\"id\": \"R1\", \"severity\": \"critical\"|\"major\"|\"minor\", \"location\": \"<file or key>\", \"issue\": \"...\", \"suggested_fix\": \"...\"}], \"summary\": \"...\"}. Use \"revise\" if there is at least one critical or major issue. Be concise and specific. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a"}12{"parentUuid":"c4af419e-60a7-4005-9aea-6e8fe7f0f7ac","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsA5sUDXu4nMFbF5HER","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016iBxAuNtxowJBqkJPqZaNw","name":"Bash","input":{"command":"cat TASK.md; 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standard errors in parentheses) and a randomised evaluation of Rhode Island's RESEA programme (average treatment effects with 95% confidence intervals). The task is to extract specified point estimates, standard errors, confidence-interval bounds and sample counts **exactly as printed**, to attach to each value a locator (source id, table number, row label, column label), to derive two quantities that the sources do not print (a 95% confidence interval from a standard error, and a standard error from a confidence interval) while stating the convention used, and to recognise which requested quantity is not reported at all. The estimands are the printed numbers themselves; nothing is re-estimated.\n\n## Inputs\n- `data/sources/arxiv-2609.27732v1.txt` — extract of Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1 [econ.GN], CC BY 4.0; contains the abstract, Section 6 (Results, with Table 6 \"Overall Average Treatment Effect on the Treated (ATT)\" by buffer distance and Table 7 \"Overall ATT by Outcome Category and Buffer Zone\") and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs are in log points; the line below each coefficient row holds standard errors in parentheses; stars denote p < 0.01 (***), p < 0.05 (**), p < 0.10 (*).\n- `data/sources/arxiv-2606.14621v1.txt` — extract of Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1 [econ.GN], CC BY 4.0; contains the abstract, Section 3 (Results, with Table 2: group means, treatment-control differences and average treatment effect (ATE) estimates with two-sided p-values and 95% confidence intervals) and Table 3 (ATE estimates under three estimators). Wages are annualised US dollars; reemployment is in percent / percentage points (pp); weeks on UI are weeks. Tables 2 and 3 were re-set in a one-row-per-outcome layout (see the extract header); values are verbatim.\n- `data/README.md` — provenance, retrieval times, licences and SHA-256 of the extracts.\n\n## Information-availability rules\n- The two extracts are the only admissible evidence; do not use remembered values from the papers or other versions.\n- \"As printed\" means the sign and the number of decimals shown in the table that the key names (e.g. `-0.431`, `0.210`, `17250` for \"17,250\"). Where a value appears in more than one table, use the table named in the key's definition.\n- Derived keys must be computed from the printed values of the named table with a stated convention (normal 1.96 or 2.0, or a t critical value); the convention must be written in `derivation_notes`.\n- A locator has the form `<source id> | Table <n> | row \"<row label>\" | column \"<column label>\"`; for derived keys, name the table the inputs came from and say \"derived\".\n- If a requested quantity is not printed in the source (and cannot be derived from printed quantities without additional assumptions), report `null` and say so in the locator (e.g. `not reported in arxiv-2609.27732v1`).\n\n## Required outputs\n- `out/results.json` with the keys (all numbers \"as printed\" unless marked derived):\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `dcr_att_5km` | number | log points | Table 6, row \"ATT\", column \"5 km\" |\n| `dcr_att_5km_se` | number | log points | Table 6, standard error of the 5 km ATT |\n| `dcr_att_3km` | number | log points | Table 6, row \"ATT\", column \"3 km\" |\n| `dcr_att_3km_se` | number | log points | Table 6, standard error of the 3 km ATT |\n| `dcr_obs_5km` | integer | region-years | Table 6, row \"Observations\", column \"5 km\" |\n| `dcr_narcotics_att_3km` | number | log points | Table 7, row \"Narcotics\", column \"3 km\" |\n| `dcr_narcotics_att_3km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_fatal_att_1km` | number | log points | Table 7, row \"Drug-related (fatal)\", column \"1 km\" |\n| `dcr_fatal_att_1km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km` | number | log points | Table 7, row \"HIV\", column \"5 km\" |\n| `dcr_hiv_att_5km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km_p_value` | number or null | — | the exact two-sided p-value of the HIV 5 km ATT if the source prints it; otherwise null |\n| `dcr_hiv_att_5km_significant_10pct` | boolean | — | whether the source marks the HIV 5 km ATT as significant at the 10% level |\n| `dcr_att_5km_ci_low`, `dcr_att_5km_ci_high` | number | log points | **derived**: 95% confidence interval of the Table 6 5 km ATT from its printed SE |\n| `ri_wage_ate` | number | USD per year | Table 2, row \"Wage\", column \"Average treatment effect estimate\", point estimate |\n| `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high` | number | USD per year | Table 2, the 95% CI bounds printed for that ATE |\n| `ri_wage_ate_se_reported` | boolean | — | whether the source prints a standard error for the wage ATE |\n| `ri_wage_ate_se_derived` | number | USD per year | **derived**: standard error implied by the printed 95% CI (state the critical value used) |\n| `ri_control_mean_wage` | number | USD per year | Table 2, row \"Wage\", column \"Control\" (mean) |\n| `ri_reemployment_ate_pp` | number | percentage points | Table 2, row \"Reemployment\", ATE point estimate |\n| `ri_weeks_ui_ate` | number | weeks | Table 2, row \"Weeks on UI\", ATE point estimate (sign as printed: negative = fewer weeks) |\n| `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high` | number | weeks | Table 2, the 95% CI bounds printed for that ATE (as printed, lower bound first) |\n| `locators` | object | — | one locator string per key above (except the two booleans) |\n| `derivation_notes` | object | — | {\"dcr_att_5km_ci\": \"<convention>\", \"ri_wage_ate_se\": \"<convention>\"} |\n| `task_id` | string | — | \"F4-conf-01\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The Uncertainty section must say which reported uncertainties are standard errors and which are confidence intervals, and what the derived quantities assume.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (text parsing is acceptable; hard-coding the numbers is acceptable only if the script also prints the matched source lines).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 72\ndrwxr-xr-x 9 simon-pierreboucher staff 288 oct. 30 01:25 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:22 ..\n-rw-r--r-- 1 simon-pierreboucher staff 10750 oct. 30 01:24 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 2230 oct. 30 01:25 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 245 oct. 30 01:23 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 5823 oct. 30 01:25 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 6552 oct. 30 01:24 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:25 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 01:25 state\n{\n \"task_id\": \"F4-conf-01\",\n \"dcr_att_5km\": -0.431,\n \"dcr_att_5km_se\": 0.21,\n \"dcr_att_3km\": -0.283,\n \"dcr_att_3km_se\": 0.279,\n \"dcr_obs_5km\": 17250,\n \"dcr_narcotics_att_3km\": 0.549,\n \"dcr_narcotics_att_3km_se\": 0.187,\n \"dcr_fatal_att_1km\": -1.207,\n \"dcr_fatal_att_1km_se\": 0.137,\n \"dcr_hiv_att_5km\": -0.34,\n \"dcr_hiv_att_5km_se\": 0.294,\n \"dcr_hiv_att_5km_p_value\": null,\n \"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_att_5km_ci_low\": -0.8426,\n \"dcr_att_5km_ci_high\": -0.0194,\n \"ri_wage_ate\": 1153,\n \"ri_wage_ate_ci_low\": 166,\n \"ri_wage_ate_ci_high\": 2141,\n \"ri_wage_ate_se_reported\": false,\n \"ri_wage_ate_se_derived\": 503.83,\n \"ri_control_mean_wage\": 33043,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n \"ri_weeks_ui_ate_ci_low\": -2.24,\n \"ri_weeks_ui_ate_ci_high\": -1.75,\n \"locators\": {\n \"dcr_att_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\"\",\n \"dcr_att_5km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" (standard error line in parentheses) | column \\\"5 km\\\"\",\n \"dcr_att_3km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\"\",\n \"dcr_att_3km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" (standard error line in parentheses) | column \\\"3 km\\\"\",\n \"dcr_obs_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"Observations\\\" | column \\\"5 km\\\"\",\n \"dcr_narcotics_att_3km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\"\",\n \"dcr_narcotics_att_3km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" (standard error line in parentheses) | column \\\"3 km\\\"\",\n \"dcr_fatal_att_1km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\"\",\n \"dcr_fatal_att_1km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" (standard error line in parentheses) | column \\\"1 km\\\"\",\n \"dcr_hiv_att_5km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\"\",\n \"dcr_hiv_att_5km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" (standard error line in parentheses) | column \\\"5 km\\\"\",\n \"dcr_hiv_att_5km_p_value\": \"not reported in arxiv-2609.27732v1 (Table 7 gives only significance stars; the HIV 5 km cell has none)\",\n \"dcr_att_5km_ci_low\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE (normal, 1.96)\",\n \"dcr_att_5km_ci_high\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE (normal, 1.96)\",\n \"ri_wage_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\"\",\n \"ri_wage_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI lower bound)\",\n \"ri_wage_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI upper bound)\",\n \"ri_wage_ate_se_derived\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | derived from printed 95% CI (width / (2 x 1.96)); SE not reported in arxiv-2606.14621v1\",\n \"ri_control_mean_wage\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Control\\\"\",\n \"ri_reemployment_ate_pp\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Reemployment\\\" | column \\\"Average treatment effect estimate\\\"\",\n \"ri_weeks_ui_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\"\",\n \"ri_weeks_ui_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI lower bound)\",\n \"ri_weeks_ui_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI upper bound)\"\n },\n \"derivation_notes\": {\n \"dcr_att_5km_ci\": \"Normal (Wald) 95% CI: ATT +/- 1.96 x SE using Table 6 printed ATT -0.431 and SE 0.210 (clustered SE, large-sample normal approximation); rounded to 4 decimals.\",\n \"ri_wage_ate_se\": \"SE = (upper - lower) / (2 x 1.96) using Table 2 printed 95% CI [166, 2,141]; assumes a symmetric normal-based CI (printed CI is symmetric around 1,153 to within rounding: 987 vs 988); rounded to 2 decimals. Using 2.0 instead would give 493.75.\"\n },\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"arxiv-2609.27732v1: Table 6 prints SE (0.210) for the 5 km ATT -0.431**, while Appendix Table A19 (CS-DR column) prints the same ATT with SE (0.218).\",\n \"handling\": \"Used Table 6 value (0.210) as the key definition names Table 6; discrepancy reported.\"\n },\n {\n \"issue\": \"arxiv-2609.27732v1: Tables 6/7 and Figure 6 state 608 never-treated control regions, the A19 note states 640; the Table 6 observation counts (16,175/16,875/17,250) equal 25 x (640 + 7/35/50 treated regions), i.e. consistent with 640 controls.\",\n \"handling\": \"Observation count reported as printed (17250); inconsistency not resolvable from the extract, reported only.\"\n },\n {\n \"issue\": \"arxiv-2609.27732v1: abstract says fatal hospitalizations 'fall by 35%', whereas Table 7 fatal 5 km ATT -0.354 log points implies about -29.8% (Section 6.1 says ~30%); prose also calls HIV estimates 'close to zero' though 3/5 km ATTs are -0.239/-0.340.\",\n \"handling\": \"Prose not used; table values extracted as printed.\"\n },\n {\n \"issue\": \"arxiv-2606.14621v1: prose reports the weeks-on-UI CI as magnitudes [1.75 weeks, 2.24 weeks]; Table 2 prints [-2.24, -1.75]. Prose also has a rendering defect 'p ¡ 0.001'.\",\n \"handling\": \"Table 2 values used (lower bound -2.24 first).\"\n }\n ],\n \"n_input_rows\": {\n \"README.md\": 10,\n \"sources/arxiv-2609.27732v1.txt\": 134,\n \"sources/arxiv-2606.14621v1.txt\": 58\n },\n \"n_analysed\": 25,\n \"random_seed\": null,\n \"baseline_dcr_att_5km_ci_2se\": [\n -0.851,\n -0.011\n ],\n \"baseline_ri_wage_ate_se_2se\": 493.75,\n \"printed_values\": {\n \"dcr_att_5km\": \"-0.431**\",\n \"dcr_att_5km_se\": \"(0.210)\",\n \"dcr_att_3km\": \"-0.283\",\n \"dcr_att_3km_se\": \"(0.279)\",\n \"dcr_obs_5km\": \"17,250\",\n \"dcr_narcotics_att_3km\": \"0.549***\",\n \"dcr_narcotics_att_3km_se\": \"(0.187)\",\n \"dcr_fatal_att_1km\": \"-1.207***\",\n \"dcr_fatal_att_1km_se\": \"(0.137)\",\n \"dcr_hiv_att_5km\": \"-0.340\",\n \"dcr_hiv_att_5km_se\": \"(0.294)\",\n \"ri_wage_cell\": \"$1,153; p=0.022; 95% CI: [166, 2,141]\",\n \"ri_control_wage_cell\": \"$33,043 (SD: 36,231)\",\n \"ri_reemployment_cell\": \"1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\",\n \"ri_weeks_ui_cell\": \"-1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\"\n }\n}[\n {\"id\":\"C1\",\"text\":\"Table 6 of arxiv-2609.27732v1 prints a 5 km ATT of -0.431** with SE (0.210), a 3 km ATT of -0.283 with SE (0.279), and 17,250 observations at 5 km.\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_3km\",\"dcr_att_3km_se\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Appendix A19 prints SE 0.218 for the same estimate; Table 6 used as the key specifies.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints narcotics 3 km 0.549*** (0.187), fatal 1 km -1.207*** (0.137), HIV 5 km -0.340 (0.294) without a star.\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"JSON stores -0.340 as -0.34; printed string kept in printed_values.\"},\n {\"id\":\"C3\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported in arxiv-2609.27732v1 (stars only).\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\"],\"status\":\"supported\",\"caveat\":\"Could be approximated under a normal assumption but is not printed.\"},\n {\"id\":\"C4\",\"text\":\"Derived 95% CI for the Table 6 5 km ATT under the normal 1.96 convention is [-0.8426, -0.0194] and excludes zero.\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"baseline_dcr_att_5km_ci_2se\"],\"status\":\"supported\",\"caveat\":\"Assumes asymptotic normality; inherits rounding of the printed SE.\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arxiv-2606.14621v1 prints a wage ATE of $1,153 with 95% CI [166, 2,141], control mean $33,043, reemployment ATE 1.47 pp, and weeks-on-UI ATE -1.99 with CI [-2.24, -1.75]; no SE is printed.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_control_mean_wage\",\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\",\"ri_wage_ate_se_reported\"],\"status\":\"supported\",\"caveat\":\"Prose gives the weeks CI as magnitudes; Table 2 values used.\"},\n {\"id\":\"C6\",\"text\":\"The SE implied by the printed wage CI (1.96 convention) is 503.83 USD/yr, consistent with the printed p=0.022.\",\"results_keys\":[\"ri_wage_ate_se_derived\",\"baseline_ri_wage_ate_se_2se\"],\"status\":\"supported\",\"caveat\":\"Assumes symmetric normal-based CI; 2.0 convention gives 493.75.\"}\n]","is_error":false}]},"uuid":"fa7de63e-e1b8-4e92-865b-08567fe72c93","timestamp":"2026-10-30T05:25:28.491Z","toolUseResult":{"stdout":"# F4-conf-01 — Extracting treatment effects and their uncertainty from two published results tables\n\n## Scientific question\nTwo open-access studies report treatment effects with uncertainty: a staggered difference-in-differences study of drug consumption room (DCR) openings in Switzerland (log hospitalisation rates; standard errors in parentheses) and a randomised evaluation of Rhode Island's RESEA programme (average treatment effects with 95% confidence intervals). The task is to extract specified point estimates, standard errors, confidence-interval bounds and sample counts **exactly as printed**, to attach to each value a locator (source id, table number, row label, column label), to derive two quantities that the sources do not print (a 95% confidence interval from a standard error, and a standard error from a confidence interval) while stating the convention used, and to recognise which requested quantity is not reported at all. The estimands are the printed numbers themselves; nothing is re-estimated.\n\n## Inputs\n- `data/sources/arxiv-2609.27732v1.txt` — extract of Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1 [econ.GN], CC BY 4.0; contains the abstract, Section 6 (Results, with Table 6 \"Overall Average Treatment Effect on the Treated (ATT)\" by buffer distance and Table 7 \"Overall ATT by Outcome Category and Buffer Zone\") and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs are in log points; the line below each coefficient row holds standard errors in parentheses; stars denote p < 0.01 (***), p < 0.05 (**), p < 0.10 (*).\n- `data/sources/arxiv-2606.14621v1.txt` — extract of Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1 [econ.GN], CC BY 4.0; contains the abstract, Section 3 (Results, with Table 2: group means, treatment-control differences and average treatment effect (ATE) estimates with two-sided p-values and 95% confidence intervals) and Table 3 (ATE estimates under three estimators). Wages are annualised US dollars; reemployment is in percent / percentage points (pp); weeks on UI are weeks. Tables 2 and 3 were re-set in a one-row-per-outcome layout (see the extract header); values are verbatim.\n- `data/README.md` — provenance, retrieval times, licences and SHA-256 of the extracts.\n\n## Information-availability rules\n- The two extracts are the only admissible evidence; do not use remembered values from the papers or other versions.\n- \"As printed\" means the sign and the number of decimals shown in the table that the key names (e.g. `-0.431`, `0.210`, `17250` for \"17,250\"). Where a value appears in more than one table, use the table named in the key's definition.\n- Derived keys must be computed from the printed values of the named table with a stated convention (normal 1.96 or 2.0, or a t critical value); the convention must be written in `derivation_notes`.\n- A locator has the form `<source id> | Table <n> | row \"<row label>\" | column \"<column label>\"`; for derived keys, name the table the inputs came from and say \"derived\".\n- If a requested quantity is not printed in the source (and cannot be derived from printed quantities without additional assumptions), report `null` and say so in the locator (e.g. `not reported in arxiv-2609.27732v1`).\n\n## Required outputs\n- `out/results.json` with the keys (all numbers \"as printed\" unless marked derived):\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `dcr_att_5km` | number | log points | Table 6, row \"ATT\", column \"5 km\" |\n| `dcr_att_5km_se` | number | log points | Table 6, standard error of the 5 km ATT |\n| `dcr_att_3km` | number | log points | Table 6, row \"ATT\", column \"3 km\" |\n| `dcr_att_3km_se` | number | log points | Table 6, standard error of the 3 km ATT |\n| `dcr_obs_5km` | integer | region-years | Table 6, row \"Observations\", column \"5 km\" |\n| `dcr_narcotics_att_3km` | number | log points | Table 7, row \"Narcotics\", column \"3 km\" |\n| `dcr_narcotics_att_3km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_fatal_att_1km` | number | log points | Table 7, row \"Drug-related (fatal)\", column \"1 km\" |\n| `dcr_fatal_att_1km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km` | number | log points | Table 7, row \"HIV\", column \"5 km\" |\n| `dcr_hiv_att_5km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km_p_value` | number or null | — | the exact two-sided p-value of the HIV 5 km ATT if the source prints it; otherwise null |\n| `dcr_hiv_att_5km_significant_10pct` | boolean | — | whether the source marks the HIV 5 km ATT as significant at the 10% level |\n| `dcr_att_5km_ci_low`, `dcr_att_5km_ci_high` | number | log points | **derived**: 95% confidence interval of the Table 6 5 km ATT from its printed SE |\n| `ri_wage_ate` | number | USD per year | Table 2, row \"Wage\", column \"Average treatment effect estimate\", point estimate |\n| `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high` | number | USD per year | Table 2, the 95% CI bounds printed for that ATE |\n| `ri_wage_ate_se_reported` | boolean | — | whether the source prints a standard error for the wage ATE |\n| `ri_wage_ate_se_derived` | number | USD per year | **derived**: standard error implied by the printed 95% CI (state the critical value used) |\n| `ri_control_mean_wage` | number | USD per year | Table 2, row \"Wage\", column \"Control\" (mean) |\n| `ri_reemployment_ate_pp` | number | percentage points | Table 2, row \"Reemployment\", ATE point estimate |\n| `ri_weeks_ui_ate` | number | weeks | Table 2, row \"Weeks on UI\", ATE point estimate (sign as printed: negative = fewer weeks) |\n| `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high` | number | weeks | Table 2, the 95% CI bounds printed for that ATE (as printed, lower bound first) |\n| `locators` | object | — | one locator string per key above (except the two booleans) |\n| `derivation_notes` | object | — | {\"dcr_att_5km_ci\": \"<convention>\", \"ri_wage_ate_se\": \"<convention>\"} |\n| `task_id` | string | — | \"F4-conf-01\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The Uncertainty section must say which reported uncertainties are standard errors and which are confidence intervals, and what the derived quantities assume.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (text parsing is acceptable; hard-coding the numbers is acceptable only if the script also prints the matched source lines).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 72\ndrwxr-xr-x 9 simon-pierreboucher staff 288 oct. 30 01:25 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:22 ..\n-rw-r--r-- 1 simon-pierreboucher staff 10750 oct. 30 01:24 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 2230 oct. 30 01:25 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 245 oct. 30 01:23 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 5823 oct. 30 01:25 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 6552 oct. 30 01:24 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:25 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 01:25 state\n{\n \"task_id\": \"F4-conf-01\",\n \"dcr_att_5km\": -0.431,\n \"dcr_att_5km_se\": 0.21,\n \"dcr_att_3km\": -0.283,\n \"dcr_att_3km_se\": 0.279,\n \"dcr_obs_5km\": 17250,\n \"dcr_narcotics_att_3km\": 0.549,\n \"dcr_narcotics_att_3km_se\": 0.187,\n \"dcr_fatal_att_1km\": -1.207,\n \"dcr_fatal_att_1km_se\": 0.137,\n \"dcr_hiv_att_5km\": -0.34,\n \"dcr_hiv_att_5km_se\": 0.294,\n \"dcr_hiv_att_5km_p_value\": null,\n \"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_att_5km_ci_low\": -0.8426,\n \"dcr_att_5km_ci_high\": -0.0194,\n \"ri_wage_ate\": 1153,\n \"ri_wage_ate_ci_low\": 166,\n \"ri_wage_ate_ci_high\": 2141,\n \"ri_wage_ate_se_reported\": false,\n \"ri_wage_ate_se_derived\": 503.83,\n \"ri_control_mean_wage\": 33043,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n \"ri_weeks_ui_ate_ci_low\": -2.24,\n \"ri_weeks_ui_ate_ci_high\": -1.75,\n \"locators\": {\n \"dcr_att_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\"\",\n \"dcr_att_5km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" (standard error line in parentheses) | column \\\"5 km\\\"\",\n \"dcr_att_3km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\"\",\n \"dcr_att_3km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" (standard error line in parentheses) | column \\\"3 km\\\"\",\n \"dcr_obs_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"Observations\\\" | column \\\"5 km\\\"\",\n \"dcr_narcotics_att_3km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\"\",\n \"dcr_narcotics_att_3km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" (standard error line in parentheses) | column \\\"3 km\\\"\",\n \"dcr_fatal_att_1km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\"\",\n \"dcr_fatal_att_1km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" (standard error line in parentheses) | column \\\"1 km\\\"\",\n \"dcr_hiv_att_5km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\"\",\n \"dcr_hiv_att_5km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" (standard error line in parentheses) | column \\\"5 km\\\"\",\n \"dcr_hiv_att_5km_p_value\": \"not reported in arxiv-2609.27732v1 (Table 7 gives only significance stars; the HIV 5 km cell has none)\",\n \"dcr_att_5km_ci_low\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE (normal, 1.96)\",\n \"dcr_att_5km_ci_high\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE (normal, 1.96)\",\n \"ri_wage_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\"\",\n \"ri_wage_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI lower bound)\",\n \"ri_wage_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI upper bound)\",\n \"ri_wage_ate_se_derived\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | derived from printed 95% CI (width / (2 x 1.96)); SE not reported in arxiv-2606.14621v1\",\n \"ri_control_mean_wage\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Control\\\"\",\n \"ri_reemployment_ate_pp\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Reemployment\\\" | column \\\"Average treatment effect estimate\\\"\",\n \"ri_weeks_ui_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\"\",\n \"ri_weeks_ui_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI lower bound)\",\n \"ri_weeks_ui_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI upper bound)\"\n },\n \"derivation_notes\": {\n \"dcr_att_5km_ci\": \"Normal (Wald) 95% CI: ATT +/- 1.96 x SE using Table 6 printed ATT -0.431 and SE 0.210 (clustered SE, large-sample normal approximation); rounded to 4 decimals.\",\n \"ri_wage_ate_se\": \"SE = (upper - lower) / (2 x 1.96) using Table 2 printed 95% CI [166, 2,141]; assumes a symmetric normal-based CI (printed CI is symmetric around 1,153 to within rounding: 987 vs 988); rounded to 2 decimals. Using 2.0 instead would give 493.75.\"\n },\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"arxiv-2609.27732v1: Table 6 prints SE (0.210) for the 5 km ATT -0.431**, while Appendix Table A19 (CS-DR column) prints the same ATT with SE (0.218).\",\n \"handling\": \"Used Table 6 value (0.210) as the key definition names Table 6; discrepancy reported.\"\n },\n {\n \"issue\": \"arxiv-2609.27732v1: Tables 6/7 and Figure 6 state 608 never-treated control regions, the A19 note states 640; the Table 6 observation counts (16,175/16,875/17,250) equal 25 x (640 + 7/35/50 treated regions), i.e. consistent with 640 controls.\",\n \"handling\": \"Observation count reported as printed (17250); inconsistency not resolvable from the extract, reported only.\"\n },\n {\n \"issue\": \"arxiv-2609.27732v1: abstract says fatal hospitalizations 'fall by 35%', whereas Table 7 fatal 5 km ATT -0.354 log points implies about -29.8% (Section 6.1 says ~30%); prose also calls HIV estimates 'close to zero' though 3/5 km ATTs are -0.239/-0.340.\",\n \"handling\": \"Prose not used; table values extracted as printed.\"\n },\n {\n \"issue\": \"arxiv-2606.14621v1: prose reports the weeks-on-UI CI as magnitudes [1.75 weeks, 2.24 weeks]; Table 2 prints [-2.24, -1.75]. Prose also has a rendering defect 'p ¡ 0.001'.\",\n \"handling\": \"Table 2 values used (lower bound -2.24 first).\"\n }\n ],\n \"n_input_rows\": {\n \"README.md\": 10,\n \"sources/arxiv-2609.27732v1.txt\": 134,\n \"sources/arxiv-2606.14621v1.txt\": 58\n },\n \"n_analysed\": 25,\n \"random_seed\": null,\n \"baseline_dcr_att_5km_ci_2se\": [\n -0.851,\n -0.011\n ],\n \"baseline_ri_wage_ate_se_2se\": 493.75,\n \"printed_values\": {\n \"dcr_att_5km\": \"-0.431**\",\n \"dcr_att_5km_se\": \"(0.210)\",\n \"dcr_att_3km\": \"-0.283\",\n \"dcr_att_3km_se\": \"(0.279)\",\n \"dcr_obs_5km\": \"17,250\",\n \"dcr_narcotics_att_3km\": \"0.549***\",\n \"dcr_narcotics_att_3km_se\": \"(0.187)\",\n \"dcr_fatal_att_1km\": \"-1.207***\",\n \"dcr_fatal_att_1km_se\": \"(0.137)\",\n \"dcr_hiv_att_5km\": \"-0.340\",\n \"dcr_hiv_att_5km_se\": \"(0.294)\",\n \"ri_wage_cell\": \"$1,153; p=0.022; 95% CI: [166, 2,141]\",\n \"ri_control_wage_cell\": \"$33,043 (SD: 36,231)\",\n \"ri_reemployment_cell\": \"1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\",\n \"ri_weeks_ui_cell\": \"-1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\"\n }\n}[\n {\"id\":\"C1\",\"text\":\"Table 6 of arxiv-2609.27732v1 prints a 5 km ATT of -0.431** with SE (0.210), a 3 km ATT of -0.283 with SE (0.279), and 17,250 observations at 5 km.\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_3km\",\"dcr_att_3km_se\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Appendix A19 prints SE 0.218 for the same estimate; Table 6 used as the key specifies.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints narcotics 3 km 0.549*** (0.187), fatal 1 km -1.207*** (0.137), HIV 5 km -0.340 (0.294) without a star.\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"JSON stores -0.340 as -0.34; printed string kept in printed_values.\"},\n {\"id\":\"C3\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported in arxiv-2609.27732v1 (stars only).\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\"],\"status\":\"supported\",\"caveat\":\"Could be approximated under a normal assumption but is not printed.\"},\n {\"id\":\"C4\",\"text\":\"Derived 95% CI for the Table 6 5 km ATT under the normal 1.96 convention is [-0.8426, -0.0194] and excludes zero.\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"baseline_dcr_att_5km_ci_2se\"],\"status\":\"supported\",\"caveat\":\"Assumes asymptotic normality; inherits rounding of the printed SE.\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arxiv-2606.14621v1 prints a wage ATE of $1,153 with 95% CI [166, 2,141], control mean $33,043, reemployment ATE 1.47 pp, and weeks-on-UI ATE -1.99 with CI [-2.24, -1.75]; no SE is printed.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_control_mean_wage\",\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\",\"ri_wage_ate_se_reported\"],\"status\":\"supported\",\"caveat\":\"Prose gives the weeks CI as magnitudes; Table 2 values used.\"},\n {\"id\":\"C6\",\"text\":\"The SE implied by the printed wage CI (1.96 convention) is 503.83 USD/yr, consistent with the printed p=0.022.\",\"results_keys\":[\"ri_wage_ate_se_derived\",\"baseline_ri_wage_ate_se_2se\"],\"status\":\"supported\",\"caveat\":\"Assumes symmetric normal-based CI; 2.0 convention gives 493.75.\"}\n]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"10e97073-359a-42ac-b7ed-df060a4ac1fd","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","hash":"2e995ae3867a731f045b3e2f3f44eeb75ed65621925a88d17375af27d62bd237","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_016iBxAuNtxowJBqkJPqZaNw","name":"Bash","input":{"command":"cat TASK.md; 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Evidence from Switzerland\nAuthors: Ana Armendariz (University of St. Gallen)\nBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\nRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\nOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\nRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n================================================================\n\n# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n\nAna Armendariz*\n\nSeptember 23, 2026\n\nThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n\nKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n\n## 6 Results\n\nThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n\nFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n\nNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n\nFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n\nTable 6: Overall Average Treatment Effect on the Treated (ATT)\n\n| 1 km | 3 km | 5 km\n\nATT | -0.399 | -0.283 | -0.431**\n\n| (0.412) | (0.279) | (0.210)\n\n% change | -32.9% | -24.7% | -35.0%\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 | 608 | 608\n\nObservations | 16,175 | 16,875 | 17,250\n\nNote: The table reports the overall ATT estimated using the do\n\n... [17787 characters truncated] ...\n\nomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n\n### 4.1 Methodological lessons (excerpt)\n\n[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n\nOutcome | Difference in means | Covariate adjusted | Fixed effects regression\nWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\nReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\nWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n\nTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n\nIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n# Data provenance — F4-conf-01\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n 10 data/README.md\n 58 data/sources/arxiv-2606.14621v1.txt\n 134 data/sources/arxiv-2609.27732v1.txt\n 202 total\nls: -R: No such file or directory\ndata:\nREADME.md\nsources","is_error":true,"tool_use_id":"toolu_01V9YjwoxACcPtKxhi18CT9n"}]},"uuid":"1b1fa917-f40d-44a1-a67c-c39d9affe8e0","timestamp":"2026-10-30T05:25:31.415Z","toolUseResult":"Error: Exit code 1\nSOURCE EXTRACT — arxiv-2609.27732v1\nTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\nAuthors: Ana Armendariz (University of St. Gallen)\nBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\nRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\nOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\nRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n================================================================\n\n# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n\nAna Armendariz*\n\nSeptember 23, 2026\n\nThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n\nKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n\n## 6 Results\n\nThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n\nFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n\nNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n\nFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n\nTable 6: Overall Average Treatment Effect on the Treated (ATT)\n\n| 1 km | 3 km | 5 km\n\nATT | -0.399 | -0.283 | -0.431**\n\n| (0.412) | (0.279) | (0.210)\n\n% change | -32.9% | -24.7% | -35.0%\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 | 608 | 608\n\nObservations | 16,175 | 16,875 | 17,250\n\nNote: The table reports the overall ATT estimated using the do\n\n... [17787 characters truncated] ...\n\nomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n\n### 4.1 Methodological lessons (excerpt)\n\n[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n\nOutcome | Difference in means | Covariate adjusted | Fixed effects regression\nWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\nReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\nWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n\nTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n\nIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n# Data provenance — F4-conf-01\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. 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Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\ndata/sources/arxiv-2606.14621v1.txt-10-Editorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n--\ndata/sources/arxiv-2606.14621v1.txt:25:[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\ndata/sources/arxiv-2606.14621v1.txt-26-\ndata/sources/arxiv-2606.14621v1.txt:27:Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\ndata/sources/arxiv-2606.14621v1.txt:28:Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\ndata/sources/arxiv-2606.14621v1.txt:29:Reemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\ndata/sources/arxiv-2606.14621v1.txt:30:Weeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\ndata/sources/arxiv-2606.14621v1.txt-31-Short-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\ndata/sources/arxiv-2606.14621v1.txt-32-Short-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\ndata/sources/arxiv-2606.14621v1.txt-33-Short-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n--\ndata/sources/arxiv-2606.14621v1.txt:35:Table 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\ndata/sources/arxiv-2606.14621v1.txt-36-\ndata/sources/arxiv-2606.14621v1.txt:37:Overall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\ndata/sources/arxiv-2606.14621v1.txt-38-\ndata/sources/arxiv-2606.14621v1.txt:39:The results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\ndata/sources/arxiv-2606.14621v1.txt-40-\ndata/sources/arxiv-2606.14621v1.txt-41-As noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\ndata/sources/arxiv-2606.14621v1.txt-42-\n--\ndata/sources/arxiv-2606.14621v1.txt:52:Wage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\ndata/sources/arxiv-2606.14621v1.txt:53:Reemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\ndata/sources/arxiv-2606.14621v1.txt:54:Weeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\ndata/sources/arxiv-2606.14621v1.txt-55-\ndata/sources/arxiv-2606.14621v1.txt:56:Table 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\ndata/sources/arxiv-2606.14621v1.txt-57-\ndata/sources/arxiv-2606.14621v1.txt:58:If we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n--\ndata/sources/arxiv-2609.27732v1.txt:7:Extract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\ndata/sources/arxiv-2609.27732v1.txt-8-Omitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\ndata/sources/arxiv-2609.27732v1.txt-9-Rendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\ndata/sources/arxiv-2609.27732v1.txt-10-================================================================\n--\ndata/sources/arxiv-2609.27732v1.txt:18:This paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\ndata/sources/arxiv-2609.27732v1.txt-19-\ndata/sources/arxiv-2609.27732v1.txt-20-Keywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\ndata/sources/arxiv-2609.27732v1.txt-21-\n--\ndata/sources/arxiv-2609.27732v1.txt:44:Control regions | 608 | 608 | 608\ndata/sources/arxiv-2609.27732v1.txt-45-\ndata/sources/arxiv-2609.27732v1.txt-46-Observations | 16,175 | 16,875 | 17,250\ndata/sources/arxiv-2609.27732v1.txt-47-\n--\ndata/sources/arxiv-2609.27732v1.txt:58:sub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\ndata/sources/arxiv-2609.27732v1.txt-59-\ndata/sources/arxiv-2609.27732v1.txt-60-Figure 7: Event study: Sub-outcome estimates,\ndata/sources/arxiv-2609.27732v1.txt-61-within 5 km, \\pm 5-year window\n--\ndata/sources/arxiv-2609.27732v1.txt:66:Table 7: Overall ATT by Outcome Category and Buffer Zone\ndata/sources/arxiv-2609.27732v1.txt-67-\ndata/sources/arxiv-2609.27732v1.txt-68-Outcome | 1 km | 3 km | 5 km\ndata/sources/arxiv-2609.27732v1.txt-69-\n--\ndata/sources/arxiv-2609.27732v1.txt:78:HIV | 0.063 | -0.239 | -0.340\ndata/sources/arxiv-2609.27732v1.txt-79-\ndata/sources/arxiv-2609.27732v1.txt-80-| (0.801) | (0.337) | (0.294)\ndata/sources/arxiv-2609.27732v1.txt-81-\ndata/sources/arxiv-2609.27732v1.txt:82:Narcotics | 1.186*** | 0.549*** | 0.390**\ndata/sources/arxiv-2609.27732v1.txt-83-\ndata/sources/arxiv-2609.27732v1.txt-84-| (0.288) | (0.187) | (0.165)\ndata/sources/arxiv-2609.27732v1.txt-85-\ndata/sources/arxiv-2609.27732v1.txt:86:Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\ndata/sources/arxiv-2609.27732v1.txt-87-\ndata/sources/arxiv-2609.27732v1.txt-88-| (0.137) | (0.215) | (0.178)\ndata/sources/arxiv-2609.27732v1.txt-89-\n--\ndata/sources/arxiv-2609.27732v1.txt:92:Control regions | 608 (never-treated) | |\ndata/sources/arxiv-2609.27732v1.txt-93-\ndata/sources/arxiv-2609.27732v1.txt-94-Note: Each cell reports the overall ATT from\ndata/sources/arxiv-2609.27732v1.txt-95-the doubly robust Callaway & Sant’Anna (2021) estimator with\n--\ndata/sources/arxiv-2609.27732v1.txt:99:HIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\ndata/sources/arxiv-2609.27732v1.txt-100-estimates are identified from a single treated region per cohort and\ndata/sources/arxiv-2609.27732v1.txt-101-should be interpreted with caution. Regions near DCRs opened before\ndata/sources/arxiv-2609.27732v1.txt-102-1998 excluded. Standard errors clustered at the Medstat region level.\n--\ndata/sources/arxiv-2609.27732v1.txt:105:Figure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\ndata/sources/arxiv-2609.27732v1.txt-106-\ndata/sources/arxiv-2609.27732v1.txt-107-### 6.1 Mechanisms\ndata/sources/arxiv-2609.27732v1.txt-108-\ndata/sources/arxiv-2609.27732v1.txt:109:The sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\ndata/sources/arxiv-2609.27732v1.txt-110-substitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\ndata/sources/arxiv-2609.27732v1.txt-111-(Kerr et al., 2017; Hedrich, 2004).\ndata/sources/arxiv-2609.27732v1.txt-112-\ndata/sources/arxiv-2609.27732v1.txt:113:The second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\ndata/sources/arxiv-2609.27732v1.txt-114-\ndata/sources/arxiv-2609.27732v1.txt-115-A third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\ndata/sources/arxiv-2609.27732v1.txt-116-\ndata/sources/arxiv-2609.27732v1.txt:117:Table A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\ndata/sources/arxiv-2609.27732v1.txt-118-\ndata/sources/arxiv-2609.27732v1.txt-119-| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\ndata/sources/arxiv-2609.27732v1.txt-120-\n--\ndata/sources/arxiv-2609.27732v1.txt:129:Control regions | 608 | | |\ndata/sources/arxiv-2609.27732v1.txt-130-\ndata/sources/arxiv-2609.27732v1.txt-131-Observations | 17,250 | | |\ndata/sources/arxiv-2609.27732v1.txt-132-","is_error":false}]},"uuid":"5c99c5a4-6abe-48f8-8231-9204375b74c3","timestamp":"2026-10-30T05:25:33.996Z","toolUseResult":{"stdout":"data/sources/arxiv-2606.14621v1.txt:7:Extract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\ndata/sources/arxiv-2606.14621v1.txt-8-Omitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\ndata/sources/arxiv-2606.14621v1.txt-9-Rendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\ndata/sources/arxiv-2606.14621v1.txt-10-Editorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n--\ndata/sources/arxiv-2606.14621v1.txt:25:[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\ndata/sources/arxiv-2606.14621v1.txt-26-\ndata/sources/arxiv-2606.14621v1.txt:27:Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\ndata/sources/arxiv-2606.14621v1.txt:28:Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\ndata/sources/arxiv-2606.14621v1.txt:29:Reemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\ndata/sources/arxiv-2606.14621v1.txt:30:Weeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\ndata/sources/arxiv-2606.14621v1.txt-31-Short-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\ndata/sources/arxiv-2606.14621v1.txt-32-Short-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\ndata/sources/arxiv-2606.14621v1.txt-33-Short-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n--\ndata/sources/arxiv-2606.14621v1.txt:35:Table 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\ndata/sources/arxiv-2606.14621v1.txt-36-\ndata/sources/arxiv-2606.14621v1.txt:37:Overall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\ndata/sources/arxiv-2606.14621v1.txt-38-\ndata/sources/arxiv-2606.14621v1.txt:39:The results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\ndata/sources/arxiv-2606.14621v1.txt-40-\ndata/sources/arxiv-2606.14621v1.txt-41-As noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\ndata/sources/arxiv-2606.14621v1.txt-42-\n--\ndata/sources/arxiv-2606.14621v1.txt:52:Wage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\ndata/sources/arxiv-2606.14621v1.txt:53:Reemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\ndata/sources/arxiv-2606.14621v1.txt:54:Weeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\ndata/sources/arxiv-2606.14621v1.txt-55-\ndata/sources/arxiv-2606.14621v1.txt:56:Table 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\ndata/sources/arxiv-2606.14621v1.txt-57-\ndata/sources/arxiv-2606.14621v1.txt:58:If we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n--\ndata/sources/arxiv-2609.27732v1.txt:7:Extract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\ndata/sources/arxiv-2609.27732v1.txt-8-Omitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\ndata/sources/arxiv-2609.27732v1.txt-9-Rendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\ndata/sources/arxiv-2609.27732v1.txt-10-================================================================\n--\ndata/sources/arxiv-2609.27732v1.txt:18:This paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\ndata/sources/arxiv-2609.27732v1.txt-19-\ndata/sources/arxiv-2609.27732v1.txt-20-Keywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\ndata/sources/arxiv-2609.27732v1.txt-21-\n--\ndata/sources/arxiv-2609.27732v1.txt:44:Control regions | 608 | 608 | 608\ndata/sources/arxiv-2609.27732v1.txt-45-\ndata/sources/arxiv-2609.27732v1.txt-46-Observations | 16,175 | 16,875 | 17,250\ndata/sources/arxiv-2609.27732v1.txt-47-\n--\ndata/sources/arxiv-2609.27732v1.txt:58:sub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\ndata/sources/arxiv-2609.27732v1.txt-59-\ndata/sources/arxiv-2609.27732v1.txt-60-Figure 7: Event study: Sub-outcome estimates,\ndata/sources/arxiv-2609.27732v1.txt-61-within 5 km, \\pm 5-year window\n--\ndata/sources/arxiv-2609.27732v1.txt:66:Table 7: Overall ATT by Outcome Category and Buffer Zone\ndata/sources/arxiv-2609.27732v1.txt-67-\ndata/sources/arxiv-2609.27732v1.txt-68-Outcome | 1 km | 3 km | 5 km\ndata/sources/arxiv-2609.27732v1.txt-69-\n--\ndata/sources/arxiv-2609.27732v1.txt:78:HIV | 0.063 | -0.239 | -0.340\ndata/sources/arxiv-2609.27732v1.txt-79-\ndata/sources/arxiv-2609.27732v1.txt-80-| (0.801) | (0.337) | (0.294)\ndata/sources/arxiv-2609.27732v1.txt-81-\ndata/sources/arxiv-2609.27732v1.txt:82:Narcotics | 1.186*** | 0.549*** | 0.390**\ndata/sources/arxiv-2609.27732v1.txt-83-\ndata/sources/arxiv-2609.27732v1.txt-84-| (0.288) | (0.187) | (0.165)\ndata/sources/arxiv-2609.27732v1.txt-85-\ndata/sources/arxiv-2609.27732v1.txt:86:Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\ndata/sources/arxiv-2609.27732v1.txt-87-\ndata/sources/arxiv-2609.27732v1.txt-88-| (0.137) | (0.215) | (0.178)\ndata/sources/arxiv-2609.27732v1.txt-89-\n--\ndata/sources/arxiv-2609.27732v1.txt:92:Control regions | 608 (never-treated) | |\ndata/sources/arxiv-2609.27732v1.txt-93-\ndata/sources/arxiv-2609.27732v1.txt-94-Note: Each cell reports the overall ATT from\ndata/sources/arxiv-2609.27732v1.txt-95-the doubly robust Callaway & Sant’Anna (2021) estimator with\n--\ndata/sources/arxiv-2609.27732v1.txt:99:HIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\ndata/sources/arxiv-2609.27732v1.txt-100-estimates are identified from a single treated region per cohort and\ndata/sources/arxiv-2609.27732v1.txt-101-should be interpreted with caution. Regions near DCRs opened before\ndata/sources/arxiv-2609.27732v1.txt-102-1998 excluded. Standard errors clustered at the Medstat region level.\n--\ndata/sources/arxiv-2609.27732v1.txt:105:Figure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\ndata/sources/arxiv-2609.27732v1.txt-106-\ndata/sources/arxiv-2609.27732v1.txt-107-### 6.1 Mechanisms\ndata/sources/arxiv-2609.27732v1.txt-108-\ndata/sources/arxiv-2609.27732v1.txt:109:The sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\ndata/sources/arxiv-2609.27732v1.txt-110-substitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\ndata/sources/arxiv-2609.27732v1.txt-111-(Kerr et al., 2017; Hedrich, 2004).\ndata/sources/arxiv-2609.27732v1.txt-112-\ndata/sources/arxiv-2609.27732v1.txt:113:The second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\ndata/sources/arxiv-2609.27732v1.txt-114-\ndata/sources/arxiv-2609.27732v1.txt-115-A third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\ndata/sources/arxiv-2609.27732v1.txt-116-\ndata/sources/arxiv-2609.27732v1.txt:117:Table A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\ndata/sources/arxiv-2609.27732v1.txt-118-\ndata/sources/arxiv-2609.27732v1.txt-119-| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\ndata/sources/arxiv-2609.27732v1.txt-120-\n--\ndata/sources/arxiv-2609.27732v1.txt:129:Control regions | 608 | | |\ndata/sources/arxiv-2609.27732v1.txt-130-\ndata/sources/arxiv-2609.27732v1.txt-131-Observations | 17,250 | | 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The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\nthroughout. Regions near DCRs that opened before 1998\n(always-treated) are excluded. Standard errors clustered at the Medstat region level in\nparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n{}^{*}p<0.10 .\n\nTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\ncorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n\nTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n\n| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n\nATT | -0.431** | -0.438** | -0.362* | -0.149***\n\n| (0.218) | (0.208) | (0.205) | (0.038)\n\n% change | -35.0% | -35.5% | -30.3% | -13.9%\n\nTreated regions | 50 | | |\n\nControl regions | 608 | | |\n\nObservations | 17,250 | | |\n\nNote: All specifications use the 5 km buffer,\nnever-treated comparison regions (640 regions), and a \\pm 5-year window\n# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables\n\n## Question\nI extracted specified treatment effects, standard errors (SEs), 95% confidence-interval (CI) bounds and sample counts **exactly as printed** in two source extracts. The first is Armendariz (2026), arXiv:2609.27732v1: Swiss drug consumption rooms (DCR), staggered difference-in-differences, Tables 6–7. The second is Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1: Rhode Island RESEA randomised trial, Table 2. Each value has a locator. I also derived two quantities the sources do not print (a 95% CI from an SE, and an SE from a 95% CI) and identified which requested quantity is not reported at all. Nothing is re-estimated.\n\n## Data and exclusions\nInputs are `data/README.md` (10 lines) and the two extracts (134 and 58 lines) (`n_input_rows`). Their SHA-256 hashes match the README (`out/state/data_audit.md`). I excluded nothing: all 25 requested value/boolean items were assessed (`n_analysed`; `out/exclusions.csv`). The inputs contain internal inconsistencies, listed in `data_issues`. None of them changes a requested cell, because each key names its table:\n- Table 6 prints the 5 km SE as (0.210). Appendix Table A19 prints (0.218) for the same estimate, −0.431**. I used Table 6.\n- The tables say there are 608 control regions and the A19 note says 640. The Table 6 observation counts equal 25 × (640 + treated regions), which fits 640.\n- Some prose disagrees with the tables: the abstract says fatal hospitalisations fall by \"35%\" against a Table 7 5 km estimate of −0.354 log points, about −29.8%; the HIV estimates are called \"close to zero\"; and the weeks-on-UI CI is given as magnitudes. I used the table values throughout.\n\n## Method\n`out/analysis.py` parses the table blocks of each extract (cells separated by \" | \"; in the DCR tables the SEs sit on the line after each coefficient). It prints every matched source line and writes `out/results.json`. The verbatim cell strings are kept under `printed_values`, because JSON numbers drop trailing zeros: 0.210 is stored as 0.21 and −0.340 as −0.34, with numeric value unchanged. Thousands separators and \"$\" are removed (17,250 → 17250). Significance comes from the stars. The script searches the DCR extract for exact \"p=\" values and finds none. No randomness is involved (`random_seed` = null).\n\n## Results\n**DCR study, Table 6** (log points):\n- 5 km ATT: `dcr_att_5km` = −0.431, printed with ** (p < 0.05); SE `dcr_att_5km_se` = 0.210.\n- 3 km ATT: `dcr_att_3km` = −0.283; SE `dcr_att_3km_se` = 0.279.\n- 5 km observations: `dcr_obs_5km` = 17250.\n\n**DCR study, Table 7** (log points):\n- Narcotics, 3 km: `dcr_narcotics_att_3km` = 0.549***, SE `dcr_narcotics_att_3km_se` = 0.187.\n- Fatal drug-related, 1 km: `dcr_fatal_att_1km` = −1.207***, SE `dcr_fatal_att_1km_se` = 0.137.\n- HIV, 5 km: `dcr_hiv_att_5km` = −0.340, SE `dcr_hiv_att_5km_se` = 0.294. This cell has no star, so `dcr_hiv_att_5km_significant_10pct` = false. The source prints no exact p-value, so `dcr_hiv_att_5km_p_value` = null.\n\n**Derived:** the 95% CI for the 5 km ATT is [`dcr_att_5km_ci_low` = −0.8426, `dcr_att_5km_ci_high` = −0.0194].\n\n**RESEA study, Table 2:**\n- Wage ATE: `ri_wage_ate` = 1153 USD/yr, 95% CI [`ri_wage_ate_ci_low` = 166, `ri_wage_ate_ci_high` = 2141].\n- The source prints no SE for this ATE (`ri_wage_ate_se_reported` = false). The SE implied by the CI is `ri_wage_ate_se_derived` = 503.83.\n- Control-group mean wage: `ri_control_mean_wage` = 33043.\n- Reemployment ATE: `ri_reemployment_ate_pp` = 1.47 pp.\n- Weeks on UI ATE: `ri_weeks_ui_ate` = −1.99 weeks, 95% CI [`ri_weeks_ui_ate_ci_low` = −2.24, `ri_weeks_ui_ate_ci_high` = −1.75].\n\nEvery key has a locator in `locators`. Feasibility is `feasibility` = \"complete\".\n\n## Uncertainty\n- **DCR (Tables 6, 7):** the reported uncertainties are **standard errors** clustered at the MedStat region level, printed in parentheses, plus significance stars. No CIs or exact p-values are printed.\n- **RESEA (Table 2):** the reported uncertainties are **95% confidence intervals** and two-sided p-values. No SEs are printed.\n- **Derived DCR CI:** ATT ± 1.96 × SE (`derivation_notes.dcr_att_5km_ci`). This assumes the estimator is approximately normal in large samples and that the printed SE is correct. Using 2.0 instead gives [−0.851, −0.011] (`baseline_dcr_att_5km_ci_2se`). Under either convention the interval excludes 0, which agrees with the ** printed in the table.\n- **Derived wage SE:** (upper − lower)/(2 × 1.96) (`derivation_notes.ri_wage_ate_se`). This assumes a symmetric, normal-based CI; the printed CI is symmetric to within rounding (987 vs 988 on each side). Using 2.0 gives 493.75 (`baseline_ri_wage_ate_se_2se`). As a check, the derived SE gives z = 2.288 and a normal two-sided p = 0.0221, which matches the printed p = 0.022 (`out/state/checks.md`).\n- **Rounding:** the derived values inherit the rounding of the printed inputs (3 decimals for the DCR SE, whole dollars for the CI bounds). Their last reported digits are therefore not precise.\n\n## Limitations and open issues\n- The exact p-value of the HIV 5 km ATT is not in the extract, so it is null. It could be approximated from the estimate and SE, but that needs a distributional assumption, and the task asks for the printed p-value only.\n- Table 6 and Appendix A19 give different SEs for the same 5 km estimate (0.210 vs 0.218), and the count of 608 control regions conflicts with the 640 implied by the A19 note and the observation counts. These are inconsistencies in the source that I cannot resolve. The 5 km CI would be slightly wider if 0.218 were correct (not computed, because the key names Table 6).\n- JSON cannot keep trailing zeros, so the printed form is given in `printed_values`.\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"Requested items (25 value/boolean keys) assessed; none excluded\",25,25,0,\"Pure extraction task; every requested item is assessed; the unreported HIV p-value is kept as null rather than excluded\"\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json","is_error":false}]},"uuid":"c43092b1-747c-4e8d-98ae-006c25fd4b02","timestamp":"2026-10-30T05:25:36.785Z","toolUseResult":{"stdout":"Note: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\nthroughout. Regions near DCRs that opened before 1998\n(always-treated) are excluded. Standard errors clustered at the Medstat region level in\nparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n{}^{*}p<0.10 .\n\nTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\ncorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n\nTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n\n| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n\nATT | -0.431** | -0.438** | -0.362* | -0.149***\n\n| (0.218) | (0.208) | (0.205) | (0.038)\n\n% change | -35.0% | -35.5% | -30.3% | -13.9%\n\nTreated regions | 50 | | |\n\nControl regions | 608 | | |\n\nObservations | 17,250 | | |\n\nNote: All specifications use the 5 km buffer,\nnever-treated comparison regions (640 regions), and a \\pm 5-year window\n# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables\n\n## Question\nI extracted specified treatment effects, standard errors (SEs), 95% confidence-interval (CI) bounds and sample counts **exactly as printed** in two source extracts. The first is Armendariz (2026), arXiv:2609.27732v1: Swiss drug consumption rooms (DCR), staggered difference-in-differences, Tables 6–7. The second is Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1: Rhode Island RESEA randomised trial, Table 2. Each value has a locator. I also derived two quantities the sources do not print (a 95% CI from an SE, and an SE from a 95% CI) and identified which requested quantity is not reported at all. Nothing is re-estimated.\n\n## Data and exclusions\nInputs are `data/README.md` (10 lines) and the two extracts (134 and 58 lines) (`n_input_rows`). Their SHA-256 hashes match the README (`out/state/data_audit.md`). I excluded nothing: all 25 requested value/boolean items were assessed (`n_analysed`; `out/exclusions.csv`). The inputs contain internal inconsistencies, listed in `data_issues`. None of them changes a requested cell, because each key names its table:\n- Table 6 prints the 5 km SE as (0.210). Appendix Table A19 prints (0.218) for the same estimate, −0.431**. I used Table 6.\n- The tables say there are 608 control regions and the A19 note says 640. The Table 6 observation counts equal 25 × (640 + treated regions), which fits 640.\n- Some prose disagrees with the tables: the abstract says fatal hospitalisations fall by \"35%\" against a Table 7 5 km estimate of −0.354 log points, about −29.8%; the HIV estimates are called \"close to zero\"; and the weeks-on-UI CI is given as magnitudes. I used the table values throughout.\n\n## Method\n`out/analysis.py` parses the table blocks of each extract (cells separated by \" | \"; in the DCR tables the SEs sit on the line after each coefficient). It prints every matched source line and writes `out/results.json`. The verbatim cell strings are kept under `printed_values`, because JSON numbers drop trailing zeros: 0.210 is stored as 0.21 and −0.340 as −0.34, with numeric value unchanged. Thousands separators and \"$\" are removed (17,250 → 17250). Significance comes from the stars. The script searches the DCR extract for exact \"p=\" values and finds none. No randomness is involved (`random_seed` = null).\n\n## Results\n**DCR study, Table 6** (log points):\n- 5 km ATT: `dcr_att_5km` = −0.431, printed with ** (p < 0.05); SE `dcr_att_5km_se` = 0.210.\n- 3 km ATT: `dcr_att_3km` = −0.283; SE `dcr_att_3km_se` = 0.279.\n- 5 km observations: `dcr_obs_5km` = 17250.\n\n**DCR study, Table 7** (log points):\n- Narcotics, 3 km: `dcr_narcotics_att_3km` = 0.549***, SE `dcr_narcotics_att_3km_se` = 0.187.\n- Fatal drug-related, 1 km: `dcr_fatal_att_1km` = −1.207***, SE `dcr_fatal_att_1km_se` = 0.137.\n- HIV, 5 km: `dcr_hiv_att_5km` = −0.340, SE `dcr_hiv_att_5km_se` = 0.294. This cell has no star, so `dcr_hiv_att_5km_significant_10pct` = false. The source prints no exact p-value, so `dcr_hiv_att_5km_p_value` = null.\n\n**Derived:** the 95% CI for the 5 km ATT is [`dcr_att_5km_ci_low` = −0.8426, `dcr_att_5km_ci_high` = −0.0194].\n\n**RESEA study, Table 2:**\n- Wage ATE: `ri_wage_ate` = 1153 USD/yr, 95% CI [`ri_wage_ate_ci_low` = 166, `ri_wage_ate_ci_high` = 2141].\n- The source prints no SE for this ATE (`ri_wage_ate_se_reported` = false). The SE implied by the CI is `ri_wage_ate_se_derived` = 503.83.\n- Control-group mean wage: `ri_control_mean_wage` = 33043.\n- Reemployment ATE: `ri_reemployment_ate_pp` = 1.47 pp.\n- Weeks on UI ATE: `ri_weeks_ui_ate` = −1.99 weeks, 95% CI [`ri_weeks_ui_ate_ci_low` = −2.24, `ri_weeks_ui_ate_ci_high` = −1.75].\n\nEvery key has a locator in `locators`. Feasibility is `feasibility` = \"complete\".\n\n## Uncertainty\n- **DCR (Tables 6, 7):** the reported uncertainties are **standard errors** clustered at the MedStat region level, printed in parentheses, plus significance stars. No CIs or exact p-values are printed.\n- **RESEA (Table 2):** the reported uncertainties are **95% confidence intervals** and two-sided p-values. No SEs are printed.\n- **Derived DCR CI:** ATT ± 1.96 × SE (`derivation_notes.dcr_att_5km_ci`). This assumes the estimator is approximately normal in large samples and that the printed SE is correct. Using 2.0 instead gives [−0.851, −0.011] (`baseline_dcr_att_5km_ci_2se`). Under either convention the interval excludes 0, which agrees with the ** printed in the table.\n- **Derived wage SE:** (upper − lower)/(2 × 1.96) (`derivation_notes.ri_wage_ate_se`). This assumes a symmetric, normal-based CI; the printed CI is symmetric to within rounding (987 vs 988 on each side). Using 2.0 gives 493.75 (`baseline_ri_wage_ate_se_2se`). As a check, the derived SE gives z = 2.288 and a normal two-sided p = 0.0221, which matches the printed p = 0.022 (`out/state/checks.md`).\n- **Rounding:** the derived values inherit the rounding of the printed inputs (3 decimals for the DCR SE, whole dollars for the CI bounds). Their last reported digits are therefore not precise.\n\n## Limitations and open issues\n- The exact p-value of the HIV 5 km ATT is not in the extract, so it is null. It could be approximated from the estimate and SE, but that needs a distributional assumption, and the task asks for the printed p-value only.\n- Table 6 and Appendix A19 give different SEs for the same 5 km estimate (0.210 vs 0.218), and the count of 608 control regions conflicts with the 640 implied by the A19 note and the observation counts. These are inconsistencies in the source that I cannot resolve. The 5 km CI would be slightly wider if 0.218 were correct (not computed, because the key names Table 6).\n- JSON cannot keep trailing zeros, so the printed form is given in `printed_values`.\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"Requested items (25 value/boolean keys) assessed; none excluded\",25,25,0,\"Pure extraction task; every requested item is assessed; the unreported HIV p-value is kept as null rather than excluded\"\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3c64e570-1d90-4dc3-88f7-a83dc52dd785","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","version":"2.1.314","gitBranch":"HEAD"}31{"parentUuid":"c43092b1-747c-4e8d-98ae-006c25fd4b02","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsAdG6hxwJa1HkLqDZo","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PkHbqexRwUY7imwjiXXFfx","name":"Bash","input":{"command":"cat out/analysis.py"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":7996,"cache_read_input_tokens":14646,"output_tokens":210,"output_tokens_details":{"thinking_tokens":26},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":7996},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXsAcy14QFosgZfAKeEx","requestRef":"90b8624d-4a62-47d2-9349-564f7b705e1f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8f348f49-9708-4437-9fa4-8231d85e2ef8","timestamp":"2026-10-30T05:25:37.014Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","version":"2.1.314","gitBranch":"HEAD"}32{"parentUuid":"8f348f49-9708-4437-9fa4-8231d85e2ef8","isSidechain":false,"promptId":"ed34e343-2d9e-4351-87b5-d6a8cd0c54c0","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PkHbqexRwUY7imwjiXXFfx","type":"tool_result","content":"\"\"\"F4-conf-01: extract printed treatment effects/uncertainty from two source extracts.\nReads only data/; writes out/results.json. No randomness.\"\"\"\nimport json, re, os, hashlib\nfrom decimal import Decimal\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nZ = 1.96 # normal critical value for 95% two-sided\n\ndef read(p):\n with open(os.path.join(D, p), encoding=\"utf-8\") as f:\n return f.read()\n\ndef wc_l(p):\n with open(os.path.join(D, p), \"rb\") as f:\n return f.read().count(b\"\\n\")\n\ndcr = read(f\"sources/{DCR_ID}.txt\").split(\"\\n\")\nri = read(f\"sources/{RI_ID}.txt\").split(\"\\n\")\n\ndef table_block(lines, title_prefix):\n i = next(k for k, l in enumerate(lines) if l.startswith(title_prefix))\n j = next(k for k in range(i + 1, len(lines)) if lines[k].startswith(\"Note:\"))\n return [(k + 1, lines[k]) for k in range(i, j) if lines[k].strip()]\n\ndef num(s):\n \"\"\"Parse printed number keeping printed decimals; returns (float, printed str).\"\"\"\n s = s.strip().replace(\"$\", \"\").replace(\",\", \"\").replace(\"*\", \"\").strip(\"()\")\n s = s.replace(\"%\", \"\")\n return (int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)), s\n\ndef dcr_cell(block, row, col):\n header = [c.strip() for c in block[1][1].split(\"|\")]\n ci = header.index(col)\n for idx, (ln, l) in enumerate(block):\n cells = [c.strip() for c in l.split(\"|\")]\n if cells[0] == row:\n raw = cells[ci]\n print(f\"[{DCR_ID} line {ln}] {l}\")\n out = {\"value\": num(raw)[0], \"stars\": raw.count(\"*\"), \"raw\": raw}\n nxt = block[idx + 1] if idx + 1 < len(block) else None\n if nxt and nxt[1].lstrip().startswith(\"| (\"):\n print(f\"[{DCR_ID} line {nxt[0]}] {nxt[1]}\")\n se_raw = [c.strip() for c in nxt[1].split(\"|\")][ci]\n out[\"se\"] = num(se_raw)[0]; out[\"se_raw\"] = se_raw\n return out\n raise KeyError(row)\n\nt6 = table_block(dcr, \"Table 6:\")\nt7 = table_block(dcr, \"Table 7:\")\nfor ln, l in t6[:2] + t7[:2]:\n print(f\"[{DCR_ID} line {ln}] {l}\")\n\na5 = dcr_cell(t6, \"ATT\", \"5 km\"); a3 = dcr_cell(t6, \"ATT\", \"3 km\")\nobs5 = dcr_cell(t6, \"Observations\", \"5 km\")\nnarc3 = dcr_cell(t7, \"Narcotics\", \"3 km\")\nfat1 = dcr_cell(t7, \"Drug-related (fatal)\", \"1 km\")\nhiv5 = dcr_cell(t7, \"HIV\", \"5 km\")\n\n# exact p-value search: any \"p=\" / \"p-value\" in DCR extract tied to HIV?\ndcr_text = \"\\n\".join(dcr)\np_hits = [l for l in dcr if re.search(r\"p\\s*=\\s*0?\\.\\d\", l)]\nhiv_p = None # stars only (none on HIV 5 km); no exact p-value printed\nprint(f\"DCR lines with an exact 'p=' value: {len(p_hits)}\")\n\n# derived CI (Table 6, 5 km); round to 4 dp (one more than the printed 3 dp)\nci_lo = round(a5[\"value\"] - Z * a5[\"se\"], 4)\nci_hi = round(a5[\"value\"] + Z * a5[\"se\"], 4)\n\n# RESEA Table 2\nt2_hdr = next(k for k, l in enumerate(ri) if l.startswith(\"Outcome | Treatment | Control\"))\nheader = [c.strip() for c in ri[t2_hdr].split(\"|\")]\nprint(f\"[{RI_ID} line {t2_hdr+1}] {ri[t2_hdr]}\")\ndef ri_row(label):\n for k in range(t2_hdr + 1, len(ri)):\n cells = [c.strip() for c in ri[k].split(\"|\")]\n if cells[0] == label:\n print(f\"[{RI_ID} line {k+1}] {ri[k]}\")\n return dict(zip(header, cells))\n raise KeyError(label)\nATE = \"Average treatment effect estimate\"\ndef parse_effect(cell):\n est = cell.split(\";\")[0]\n est = re.sub(r\"\\s*(pp|weeks|wks)$\", \"\", est.strip())\n m = re.search(r\"95% CI: \\[(.+?),\\s*(-?[\\d.]+(?:,\\d{3})*(?:\\.\\d+)?)\\]\", cell)\n lo, hi = m.group(1), m.group(2)\n se_printed = bool(re.search(r\"\\bSE\\b|standard error\", cell, re.I))\n return num(est)[0], num(lo)[0], num(hi)[0], se_printed\nw = ri_row(\"Wage\"); r = ri_row(\"Reemployment\"); u = ri_row(\"Weeks on UI\")\nw_ate, w_lo, w_hi, w_se_printed = parse_effect(w[ATE])\nr_ate, _, _, _ = parse_effect(r[ATE])\nu_ate, u_lo, u_hi, _ = parse_effect(u[ATE])\nctrl_mean = num(w[\"Control\"].split(\"(\")[0])[0]\nw_se = round((w_hi - w_lo) / (2 * Z), 2)\n\nS6 = f\"{DCR_ID} | Table 6\"; S7 = f\"{DCR_ID} | Table 7\"; S2 = f\"{RI_ID} | Table 2\"\nloc = {\n \"dcr_att_5km\": f'{S6} | row \"ATT\" | column \"5 km\"',\n \"dcr_att_5km_se\": f'{S6} | row \"ATT\" (standard error line in parentheses) | column \"5 km\"',\n \"dcr_att_3km\": f'{S6} | row \"ATT\" | column \"3 km\"',\n \"dcr_att_3km_se\": f'{S6} | row \"ATT\" (standard error line in parentheses) | column \"3 km\"',\n \"dcr_obs_5km\": f'{S6} | row \"Observations\" | column \"5 km\"',\n \"dcr_narcotics_att_3km\": f'{S7} | row \"Narcotics\" | column \"3 km\"',\n \"dcr_narcotics_att_3km_se\": f'{S7} | row \"Narcotics\" (standard error line in parentheses) | column \"3 km\"',\n \"dcr_fatal_att_1km\": f'{S7} | row \"Drug-related (fatal)\" | column \"1 km\"',\n \"dcr_fatal_att_1km_se\": f'{S7} | row \"Drug-related (fatal)\" (standard error line in parentheses) | column \"1 km\"',\n \"dcr_hiv_att_5km\": f'{S7} | row \"HIV\" | column \"5 km\"',\n \"dcr_hiv_att_5km_se\": f'{S7} | row \"HIV\" (standard error line in parentheses) | column \"5 km\"',\n \"dcr_hiv_att_5km_p_value\": f\"not reported in {DCR_ID} (Table 7 gives only significance stars; the HIV 5 km cell has none)\",\n \"dcr_att_5km_ci_low\": f'{S6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (normal, 1.96)',\n \"dcr_att_5km_ci_high\": f'{S6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (normal, 1.96)',\n \"ri_wage_ate\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\"',\n \"ri_wage_ate_ci_low\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n \"ri_wage_ate_ci_high\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n \"ri_wage_ate_se_derived\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" | derived from printed 95% CI (width / (2 x 1.96)); SE not reported in {RI_ID}',\n \"ri_control_mean_wage\": f'{S2} | row \"Wage\" | column \"Control\"',\n \"ri_reemployment_ate_pp\": f'{S2} | row \"Reemployment\" | column \"Average treatment effect estimate\"',\n \"ri_weeks_ui_ate\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\"',\n \"ri_weeks_ui_ate_ci_low\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n \"ri_weeks_ui_ate_ci_high\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n}\n\nissues = [\n {\"issue\": f\"{DCR_ID}: Table 6 prints SE (0.210) for the 5 km ATT -0.431**, while Appendix Table A19 (CS-DR column) prints the same ATT with SE (0.218).\",\n \"handling\": \"Used Table 6 value (0.210) as the key definition names Table 6; discrepancy reported.\"},\n {\"issue\": f\"{DCR_ID}: Tables 6/7 and Figure 6 state 608 never-treated control regions, the A19 note states 640; the Table 6 observation counts (16,175/16,875/17,250) equal 25 x (640 + 7/35/50 treated regions), i.e. consistent with 640 controls.\",\n \"handling\": \"Observation count reported as printed (17250); inconsistency not resolvable from the extract, reported only.\"},\n {\"issue\": f\"{DCR_ID}: abstract says fatal hospitalizations 'fall by 35%', whereas Table 7 fatal 5 km ATT -0.354 log points implies about -29.8% (Section 6.1 says ~30%); prose also calls HIV estimates 'close to zero' though 3/5 km ATTs are -0.239/-0.340.\",\n \"handling\": \"Prose not used; table values extracted as printed.\"},\n {\"issue\": f\"{RI_ID}: prose reports the weeks-on-UI CI as magnitudes [1.75 weeks, 2.24 weeks]; Table 2 prints [-2.24, -1.75]. Prose also has a rendering defect 'p ¡ 0.001'.\",\n \"handling\": \"Table 2 values used (lower bound -2.24 first).\"},\n]\n\nres = {\n \"task_id\": \"F4-conf-01\",\n \"dcr_att_5km\": a5[\"value\"], \"dcr_att_5km_se\": a5[\"se\"],\n \"dcr_att_3km\": a3[\"value\"], \"dcr_att_3km_se\": a3[\"se\"],\n \"dcr_obs_5km\": int(obs5[\"value\"]),\n \"dcr_narcotics_att_3km\": narc3[\"value\"], \"dcr_narcotics_att_3km_se\": narc3[\"se\"],\n \"dcr_fatal_att_1km\": fat1[\"value\"], \"dcr_fatal_att_1km_se\": fat1[\"se\"],\n \"dcr_hiv_att_5km\": hiv5[\"value\"], \"dcr_hiv_att_5km_se\": hiv5[\"se\"],\n \"dcr_hiv_att_5km_p_value\": hiv_p,\n \"dcr_hiv_att_5km_significant_10pct\": hiv5[\"stars\"] >= 1,\n \"dcr_att_5km_ci_low\": ci_lo, \"dcr_att_5km_ci_high\": ci_hi,\n \"ri_wage_ate\": w_ate, \"ri_wage_ate_ci_low\": w_lo, \"ri_wage_ate_ci_high\": w_hi,\n \"ri_wage_ate_se_reported\": w_se_printed,\n \"ri_wage_ate_se_derived\": w_se,\n \"ri_control_mean_wage\": ctrl_mean,\n \"ri_reemployment_ate_pp\": r_ate,\n \"ri_weeks_ui_ate\": u_ate, \"ri_weeks_ui_ate_ci_low\": u_lo, \"ri_weeks_ui_ate_ci_high\": u_hi,\n \"locators\": loc,\n \"derivation_notes\": {\n \"dcr_att_5km_ci\": \"Normal (Wald) 95% CI: ATT +/- 1.96 x SE using Table 6 printed ATT -0.431 and SE 0.210 (clustered SE, large-sample normal approximation); rounded to 4 decimals.\",\n \"ri_wage_ate_se\": \"SE = (upper - lower) / (2 x 1.96) using Table 2 printed 95% CI [166, 2,141]; assumes a symmetric normal-based CI (printed CI is symmetric around 1,153 to within rounding: 987 vs 988); rounded to 2 decimals. Using 2.0 instead would give 493.75.\"},\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"README.md\": wc_l(\"README.md\"),\n f\"sources/{DCR_ID}.txt\": wc_l(f\"sources/{DCR_ID}.txt\"),\n f\"sources/{RI_ID}.txt\": wc_l(f\"sources/{RI_ID}.txt\")},\n \"n_analysed\": 25,\n \"random_seed\": None,\n}\n# baseline: cruder \"2-sigma\" convention for the two derived quantities (sensitivity reference)\nres[\"baseline_dcr_att_5km_ci_2se\"] = [round(a5[\"value\"] - 2.0 * a5[\"se\"], 4), round(a5[\"value\"] + 2.0 * a5[\"se\"], 4)]\nres[\"baseline_ri_wage_ate_se_2se\"] = round((w_hi - w_lo) / 4.0, 2)\n# printed strings (JSON numbers drop trailing zeros, e.g. 0.210 -> 0.21)\nres[\"printed_values\"] = {\n \"dcr_att_5km\": a5[\"raw\"], \"dcr_att_5km_se\": a5[\"se_raw\"],\n \"dcr_att_3km\": a3[\"raw\"], \"dcr_att_3km_se\": a3[\"se_raw\"],\n \"dcr_obs_5km\": obs5[\"raw\"],\n \"dcr_narcotics_att_3km\": narc3[\"raw\"], \"dcr_narcotics_att_3km_se\": narc3[\"se_raw\"],\n \"dcr_fatal_att_1km\": fat1[\"raw\"], \"dcr_fatal_att_1km_se\": fat1[\"se_raw\"],\n \"dcr_hiv_att_5km\": hiv5[\"raw\"], \"dcr_hiv_att_5km_se\": hiv5[\"se_raw\"],\n \"ri_wage_cell\": w[ATE], \"ri_control_wage_cell\": w[\"Control\"],\n \"ri_reemployment_cell\": r[ATE], \"ri_weeks_ui_cell\": u[ATE],\n}\n# consistency diagnostics (printed only)\nimport math\nzw = w_ate / w_se\nprint(f\"Check: wage ATE z={zw:.3f}, implied two-sided normal p={math.erfc(abs(zw)/math.sqrt(2)):.4f} (printed p=0.022)\")\nprint(f\"Check: DCR 5km |t|={abs(a5['value'])/a5['se']:.3f} (printed **, i.e. p<0.05)\")\nprint(f\"Check: HIV 5km |t|={abs(hiv5['value'])/hiv5['se']:.3f} (no star)\")\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"locators\", \"derivation_notes\", \"data_issues\")}, indent=1))","is_error":false}]},"uuid":"9ccb7841-c297-4a3f-a65f-0e5db1ad30ff","timestamp":"2026-10-30T05:25:37.054Z","toolUseResult":{"stdout":"\"\"\"F4-conf-01: extract printed treatment effects/uncertainty from two source extracts.\nReads only data/; writes out/results.json. No randomness.\"\"\"\nimport json, re, os, hashlib\nfrom decimal import Decimal\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nZ = 1.96 # normal critical value for 95% two-sided\n\ndef read(p):\n with open(os.path.join(D, p), encoding=\"utf-8\") as f:\n return f.read()\n\ndef wc_l(p):\n with open(os.path.join(D, p), \"rb\") as f:\n return f.read().count(b\"\\n\")\n\ndcr = read(f\"sources/{DCR_ID}.txt\").split(\"\\n\")\nri = read(f\"sources/{RI_ID}.txt\").split(\"\\n\")\n\ndef table_block(lines, title_prefix):\n i = next(k for k, l in enumerate(lines) if l.startswith(title_prefix))\n j = next(k for k in range(i + 1, len(lines)) if lines[k].startswith(\"Note:\"))\n return [(k + 1, lines[k]) for k in range(i, j) if lines[k].strip()]\n\ndef num(s):\n \"\"\"Parse printed number keeping printed decimals; returns (float, printed str).\"\"\"\n s = s.strip().replace(\"$\", \"\").replace(\",\", \"\").replace(\"*\", \"\").strip(\"()\")\n s = s.replace(\"%\", \"\")\n return (int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)), s\n\ndef dcr_cell(block, row, col):\n header = [c.strip() for c in block[1][1].split(\"|\")]\n ci = header.index(col)\n for idx, (ln, l) in enumerate(block):\n cells = [c.strip() for c in l.split(\"|\")]\n if cells[0] == row:\n raw = cells[ci]\n print(f\"[{DCR_ID} line {ln}] {l}\")\n out = {\"value\": num(raw)[0], \"stars\": raw.count(\"*\"), \"raw\": raw}\n nxt = block[idx + 1] if idx + 1 < len(block) else None\n if nxt and nxt[1].lstrip().startswith(\"| (\"):\n print(f\"[{DCR_ID} line {nxt[0]}] {nxt[1]}\")\n se_raw = [c.strip() for c in nxt[1].split(\"|\")][ci]\n out[\"se\"] = num(se_raw)[0]; out[\"se_raw\"] = se_raw\n return out\n raise KeyError(row)\n\nt6 = table_block(dcr, \"Table 6:\")\nt7 = table_block(dcr, \"Table 7:\")\nfor ln, l in t6[:2] + t7[:2]:\n print(f\"[{DCR_ID} line {ln}] {l}\")\n\na5 = dcr_cell(t6, \"ATT\", \"5 km\"); a3 = dcr_cell(t6, \"ATT\", \"3 km\")\nobs5 = dcr_cell(t6, \"Observations\", \"5 km\")\nnarc3 = dcr_cell(t7, \"Narcotics\", \"3 km\")\nfat1 = dcr_cell(t7, \"Drug-related (fatal)\", \"1 km\")\nhiv5 = dcr_cell(t7, \"HIV\", \"5 km\")\n\n# exact p-value search: any \"p=\" / \"p-value\" in DCR extract tied to HIV?\ndcr_text = \"\\n\".join(dcr)\np_hits = [l for l in dcr if re.search(r\"p\\s*=\\s*0?\\.\\d\", l)]\nhiv_p = None # stars only (none on HIV 5 km); no exact p-value printed\nprint(f\"DCR lines with an exact 'p=' value: {len(p_hits)}\")\n\n# derived CI (Table 6, 5 km); round to 4 dp (one more than the printed 3 dp)\nci_lo = round(a5[\"value\"] - Z * a5[\"se\"], 4)\nci_hi = round(a5[\"value\"] + Z * a5[\"se\"], 4)\n\n# RESEA Table 2\nt2_hdr = next(k for k, l in enumerate(ri) if l.startswith(\"Outcome | Treatment | Control\"))\nheader = [c.strip() for c in ri[t2_hdr].split(\"|\")]\nprint(f\"[{RI_ID} line {t2_hdr+1}] {ri[t2_hdr]}\")\ndef ri_row(label):\n for k in range(t2_hdr + 1, len(ri)):\n cells = [c.strip() for c in ri[k].split(\"|\")]\n if cells[0] == label:\n print(f\"[{RI_ID} line {k+1}] {ri[k]}\")\n return dict(zip(header, cells))\n raise KeyError(label)\nATE = \"Average treatment effect estimate\"\ndef parse_effect(cell):\n est = cell.split(\";\")[0]\n est = re.sub(r\"\\s*(pp|weeks|wks)$\", \"\", est.strip())\n m = re.search(r\"95% CI: \\[(.+?),\\s*(-?[\\d.]+(?:,\\d{3})*(?:\\.\\d+)?)\\]\", cell)\n lo, hi = m.group(1), m.group(2)\n se_printed = bool(re.search(r\"\\bSE\\b|standard error\", cell, re.I))\n return num(est)[0], num(lo)[0], num(hi)[0], se_printed\nw = ri_row(\"Wage\"); r = ri_row(\"Reemployment\"); u = ri_row(\"Weeks on UI\")\nw_ate, w_lo, w_hi, w_se_printed = parse_effect(w[ATE])\nr_ate, _, _, _ = parse_effect(r[ATE])\nu_ate, u_lo, u_hi, _ = parse_effect(u[ATE])\nctrl_mean = num(w[\"Control\"].split(\"(\")[0])[0]\nw_se = round((w_hi - w_lo) / (2 * Z), 2)\n\nS6 = f\"{DCR_ID} | Table 6\"; S7 = f\"{DCR_ID} | Table 7\"; S2 = f\"{RI_ID} | Table 2\"\nloc = {\n \"dcr_att_5km\": f'{S6} | row \"ATT\" | column \"5 km\"',\n \"dcr_att_5km_se\": f'{S6} | row \"ATT\" (standard error line in parentheses) | column \"5 km\"',\n \"dcr_att_3km\": f'{S6} | row \"ATT\" | column \"3 km\"',\n \"dcr_att_3km_se\": f'{S6} | row \"ATT\" (standard error line in parentheses) | column \"3 km\"',\n \"dcr_obs_5km\": f'{S6} | row \"Observations\" | column \"5 km\"',\n \"dcr_narcotics_att_3km\": f'{S7} | row \"Narcotics\" | column \"3 km\"',\n \"dcr_narcotics_att_3km_se\": f'{S7} | row \"Narcotics\" (standard error line in parentheses) | column \"3 km\"',\n \"dcr_fatal_att_1km\": f'{S7} | row \"Drug-related (fatal)\" | column \"1 km\"',\n \"dcr_fatal_att_1km_se\": f'{S7} | row \"Drug-related (fatal)\" (standard error line in parentheses) | column \"1 km\"',\n \"dcr_hiv_att_5km\": f'{S7} | row \"HIV\" | column \"5 km\"',\n \"dcr_hiv_att_5km_se\": f'{S7} | row \"HIV\" (standard error line in parentheses) | column \"5 km\"',\n \"dcr_hiv_att_5km_p_value\": f\"not reported in {DCR_ID} (Table 7 gives only significance stars; the HIV 5 km cell has none)\",\n \"dcr_att_5km_ci_low\": f'{S6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (normal, 1.96)',\n \"dcr_att_5km_ci_high\": f'{S6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (normal, 1.96)',\n \"ri_wage_ate\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\"',\n \"ri_wage_ate_ci_low\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n \"ri_wage_ate_ci_high\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n \"ri_wage_ate_se_derived\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" | derived from printed 95% CI (width / (2 x 1.96)); SE not reported in {RI_ID}',\n \"ri_control_mean_wage\": f'{S2} | row \"Wage\" | column \"Control\"',\n \"ri_reemployment_ate_pp\": f'{S2} | row \"Reemployment\" | column \"Average treatment effect estimate\"',\n \"ri_weeks_ui_ate\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\"',\n \"ri_weeks_ui_ate_ci_low\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n \"ri_weeks_ui_ate_ci_high\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n}\n\nissues = [\n {\"issue\": f\"{DCR_ID}: Table 6 prints SE (0.210) for the 5 km ATT -0.431**, while Appendix Table A19 (CS-DR column) prints the same ATT with SE (0.218).\",\n \"handling\": \"Used Table 6 value (0.210) as the key definition names Table 6; discrepancy reported.\"},\n {\"issue\": f\"{DCR_ID}: Tables 6/7 and Figure 6 state 608 never-treated control regions, the A19 note states 640; the Table 6 observation counts (16,175/16,875/17,250) equal 25 x (640 + 7/35/50 treated regions), i.e. consistent with 640 controls.\",\n \"handling\": \"Observation count reported as printed (17250); inconsistency not resolvable from the extract, reported only.\"},\n {\"issue\": f\"{DCR_ID}: abstract says fatal hospitalizations 'fall by 35%', whereas Table 7 fatal 5 km ATT -0.354 log points implies about -29.8% (Section 6.1 says ~30%); prose also calls HIV estimates 'close to zero' though 3/5 km ATTs are -0.239/-0.340.\",\n \"handling\": \"Prose not used; table values extracted as printed.\"},\n {\"issue\": f\"{RI_ID}: prose reports the weeks-on-UI CI as magnitudes [1.75 weeks, 2.24 weeks]; Table 2 prints [-2.24, -1.75]. Prose also has a rendering defect 'p ¡ 0.001'.\",\n \"handling\": \"Table 2 values used (lower bound -2.24 first).\"},\n]\n\nres = {\n \"task_id\": \"F4-conf-01\",\n \"dcr_att_5km\": a5[\"value\"], \"dcr_att_5km_se\": a5[\"se\"],\n \"dcr_att_3km\": a3[\"value\"], \"dcr_att_3km_se\": a3[\"se\"],\n \"dcr_obs_5km\": int(obs5[\"value\"]),\n \"dcr_narcotics_att_3km\": narc3[\"value\"], \"dcr_narcotics_att_3km_se\": narc3[\"se\"],\n \"dcr_fatal_att_1km\": fat1[\"value\"], \"dcr_fatal_att_1km_se\": fat1[\"se\"],\n \"dcr_hiv_att_5km\": hiv5[\"value\"], \"dcr_hiv_att_5km_se\": hiv5[\"se\"],\n \"dcr_hiv_att_5km_p_value\": hiv_p,\n \"dcr_hiv_att_5km_significant_10pct\": hiv5[\"stars\"] >= 1,\n \"dcr_att_5km_ci_low\": ci_lo, \"dcr_att_5km_ci_high\": ci_hi,\n \"ri_wage_ate\": w_ate, \"ri_wage_ate_ci_low\": w_lo, \"ri_wage_ate_ci_high\": w_hi,\n \"ri_wage_ate_se_reported\": w_se_printed,\n \"ri_wage_ate_se_derived\": w_se,\n \"ri_control_mean_wage\": ctrl_mean,\n \"ri_reemployment_ate_pp\": r_ate,\n \"ri_weeks_ui_ate\": u_ate, \"ri_weeks_ui_ate_ci_low\": u_lo, \"ri_weeks_ui_ate_ci_high\": u_hi,\n \"locators\": loc,\n \"derivation_notes\": {\n \"dcr_att_5km_ci\": \"Normal (Wald) 95% CI: ATT +/- 1.96 x SE using Table 6 printed ATT -0.431 and SE 0.210 (clustered SE, large-sample normal approximation); rounded to 4 decimals.\",\n \"ri_wage_ate_se\": \"SE = (upper - lower) / (2 x 1.96) using Table 2 printed 95% CI [166, 2,141]; assumes a symmetric normal-based CI (printed CI is symmetric around 1,153 to within rounding: 987 vs 988); rounded to 2 decimals. Using 2.0 instead would give 493.75.\"},\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"README.md\": wc_l(\"README.md\"),\n f\"sources/{DCR_ID}.txt\": wc_l(f\"sources/{DCR_ID}.txt\"),\n f\"sources/{RI_ID}.txt\": wc_l(f\"sources/{RI_ID}.txt\")},\n \"n_analysed\": 25,\n \"random_seed\": None,\n}\n# baseline: cruder \"2-sigma\" convention for the two derived quantities (sensitivity reference)\nres[\"baseline_dcr_att_5km_ci_2se\"] = [round(a5[\"value\"] - 2.0 * a5[\"se\"], 4), round(a5[\"value\"] + 2.0 * a5[\"se\"], 4)]\nres[\"baseline_ri_wage_ate_se_2se\"] = round((w_hi - w_lo) / 4.0, 2)\n# printed strings (JSON numbers drop trailing zeros, e.g. 0.210 -> 0.21)\nres[\"printed_values\"] = {\n \"dcr_att_5km\": a5[\"raw\"], \"dcr_att_5km_se\": a5[\"se_raw\"],\n \"dcr_att_3km\": a3[\"raw\"], \"dcr_att_3km_se\": a3[\"se_raw\"],\n \"dcr_obs_5km\": obs5[\"raw\"],\n \"dcr_narcotics_att_3km\": narc3[\"raw\"], \"dcr_narcotics_att_3km_se\": narc3[\"se_raw\"],\n \"dcr_fatal_att_1km\": fat1[\"raw\"], \"dcr_fatal_att_1km_se\": fat1[\"se_raw\"],\n \"dcr_hiv_att_5km\": hiv5[\"raw\"], \"dcr_hiv_att_5km_se\": hiv5[\"se_raw\"],\n \"ri_wage_cell\": w[ATE], \"ri_control_wage_cell\": w[\"Control\"],\n \"ri_reemployment_cell\": r[ATE], \"ri_weeks_ui_cell\": u[ATE],\n}\n# consistency diagnostics (printed only)\nimport math\nzw = w_ate / w_se\nprint(f\"Check: wage ATE z={zw:.3f}, implied two-sided normal p={math.erfc(abs(zw)/math.sqrt(2)):.4f} (printed p=0.022)\")\nprint(f\"Check: DCR 5km |t|={abs(a5['value'])/a5['se']:.3f} (printed **, i.e. p<0.05)\")\nprint(f\"Check: HIV 5km |t|={abs(hiv5['value'])/hiv5['se']:.3f} (no star)\")\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"locators\", \"derivation_notes\", \"data_issues\")}, indent=1))","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"8f348f49-9708-4437-9fa4-8231d85e2ef8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","hash":"5b20a01fad0ff89af054f0c977208dff3b28c446c8776762ffcb1414386834f2","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01XSDwnN5RNBaN3BN3yJ9TM9","name":"Bash","input":{"command":"sed -n 47,56p data/sources/arxiv-2609.27732v1.txt; sed -n 117,134p data/sources/arxiv-2609.27732v1.txt; cat out/report.md; cat out/exclusions.csv; ls out/state"}},{"type":"tool_use","id":"toolu_01PkHbqexRwUY7imwjiXXFfx","name":"Bash","input":{"command":"cat out/analysis.py"}}]}}34{"type":"api-request-blob","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","hash":"01627002826e1bf84a8e9df56f5b083dc8766c00160602c7ee8a294710cfa9ef","message":{"role":"user","content":[{"tool_use_id":"toolu_01XSDwnN5RNBaN3BN3yJ9TM9","type":"tool_result","content":"Note: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\nthroughout. Regions near DCRs that opened before 1998\n(always-treated) are excluded. Standard errors clustered at the Medstat region level in\nparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n{}^{*}p<0.10 .\n\nTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\ncorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n\nTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n\n| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n\nATT | -0.431** | -0.438** | -0.362* | -0.149***\n\n| (0.218) | (0.208) | (0.205) | (0.038)\n\n% change | -35.0% | -35.5% | -30.3% | -13.9%\n\nTreated regions | 50 | | |\n\nControl regions | 608 | | |\n\nObservations | 17,250 | | |\n\nNote: All specifications use the 5 km buffer,\nnever-treated comparison regions (640 regions), and a \\pm 5-year window\n# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables\n\n## Question\nI extracted specified treatment effects, standard errors (SEs), 95% confidence-interval (CI) bounds and sample counts **exactly as printed** in two source extracts. The first is Armendariz (2026), arXiv:2609.27732v1: Swiss drug consumption rooms (DCR), staggered difference-in-differences, Tables 6–7. The second is Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1: Rhode Island RESEA randomised trial, Table 2. Each value has a locator. I also derived two quantities the sources do not print (a 95% CI from an SE, and an SE from a 95% CI) and identified which requested quantity is not reported at all. Nothing is re-estimated.\n\n## Data and exclusions\nInputs are `data/README.md` (10 lines) and the two extracts (134 and 58 lines) (`n_input_rows`). Their SHA-256 hashes match the README (`out/state/data_audit.md`). I excluded nothing: all 25 requested value/boolean items were assessed (`n_analysed`; `out/exclusions.csv`). The inputs contain internal inconsistencies, listed in `data_issues`. None of them changes a requested cell, because each key names its table:\n- Table 6 prints the 5 km SE as (0.210). Appendix Table A19 prints (0.218) for the same estimate, −0.431**. I used Table 6.\n- The tables say there are 608 control regions and the A19 note says 640. The Table 6 observation counts equal 25 × (640 + treated regions), which fits 640.\n- Some prose disagrees with the tables: the abstract says fatal hospitalisations fall by \"35%\" against a Table 7 5 km estimate of −0.354 log points, about −29.8%; the HIV estimates are called \"close to zero\"; and the weeks-on-UI CI is given as magnitudes. I used the table values throughout.\n\n## Method\n`out/analysis.py` parses the table blocks of each extract (cells separated by \" | \"; in the DCR tables the SEs sit on the line after each coefficient). It prints every matched source line and writes `out/results.json`. The verbatim cell strings are kept under `printed_values`, because JSON numbers drop trailing zeros: 0.210 is stored as 0.21 and −0.340 as −0.34, with numeric value unchanged. Thousands separators and \"$\" are removed (17,250 → 17250). Significance comes from the stars. The script searches the DCR extract for exact \"p=\" values and finds none. No randomness is involved (`random_seed` = null).\n\n## Results\n**DCR study, Table 6** (log points):\n- 5 km ATT: `dcr_att_5km` = −0.431, printed with ** (p < 0.05); SE `dcr_att_5km_se` = 0.210.\n- 3 km ATT: `dcr_att_3km` = −0.283; SE `dcr_att_3km_se` = 0.279.\n- 5 km observations: `dcr_obs_5km` = 17250.\n\n**DCR study, Table 7** (log points):\n- Narcotics, 3 km: `dcr_narcotics_att_3km` = 0.549***, SE `dcr_narcotics_att_3km_se` = 0.187.\n- Fatal drug-related, 1 km: `dcr_fatal_att_1km` = −1.207***, SE `dcr_fatal_att_1km_se` = 0.137.\n- HIV, 5 km: `dcr_hiv_att_5km` = −0.340, SE `dcr_hiv_att_5km_se` = 0.294. This cell has no star, so `dcr_hiv_att_5km_significant_10pct` = false. The source prints no exact p-value, so `dcr_hiv_att_5km_p_value` = null.\n\n**Derived:** the 95% CI for the 5 km ATT is [`dcr_att_5km_ci_low` = −0.8426, `dcr_att_5km_ci_high` = −0.0194].\n\n**RESEA study, Table 2:**\n- Wage ATE: `ri_wage_ate` = 1153 USD/yr, 95% CI [`ri_wage_ate_ci_low` = 166, `ri_wage_ate_ci_high` = 2141].\n- The source prints no SE for this ATE (`ri_wage_ate_se_reported` = false). The SE implied by the CI is `ri_wage_ate_se_derived` = 503.83.\n- Control-group mean wage: `ri_control_mean_wage` = 33043.\n- Reemployment ATE: `ri_reemployment_ate_pp` = 1.47 pp.\n- Weeks on UI ATE: `ri_weeks_ui_ate` = −1.99 weeks, 95% CI [`ri_weeks_ui_ate_ci_low` = −2.24, `ri_weeks_ui_ate_ci_high` = −1.75].\n\nEvery key has a locator in `locators`. Feasibility is `feasibility` = \"complete\".\n\n## Uncertainty\n- **DCR (Tables 6, 7):** the reported uncertainties are **standard errors** clustered at the MedStat region level, printed in parentheses, plus significance stars. No CIs or exact p-values are printed.\n- **RESEA (Table 2):** the reported uncertainties are **95% confidence intervals** and two-sided p-values. No SEs are printed.\n- **Derived DCR CI:** ATT ± 1.96 × SE (`derivation_notes.dcr_att_5km_ci`). This assumes the estimator is approximately normal in large samples and that the printed SE is correct. Using 2.0 instead gives [−0.851, −0.011] (`baseline_dcr_att_5km_ci_2se`). Under either convention the interval excludes 0, which agrees with the ** printed in the table.\n- **Derived wage SE:** (upper − lower)/(2 × 1.96) (`derivation_notes.ri_wage_ate_se`). This assumes a symmetric, normal-based CI; the printed CI is symmetric to within rounding (987 vs 988 on each side). Using 2.0 gives 493.75 (`baseline_ri_wage_ate_se_2se`). As a check, the derived SE gives z = 2.288 and a normal two-sided p = 0.0221, which matches the printed p = 0.022 (`out/state/checks.md`).\n- **Rounding:** the derived values inherit the rounding of the printed inputs (3 decimals for the DCR SE, whole dollars for the CI bounds). Their last reported digits are therefore not precise.\n\n## Limitations and open issues\n- The exact p-value of the HIV 5 km ATT is not in the extract, so it is null. It could be approximated from the estimate and SE, but that needs a distributional assumption, and the task asks for the printed p-value only.\n- Table 6 and Appendix A19 give different SEs for the same 5 km estimate (0.210 vs 0.218), and the count of 608 control regions conflicts with the 640 implied by the A19 note and the observation counts. These are inconsistencies in the source that I cannot resolve. The 5 km CI would be slightly wider if 0.218 were correct (not computed, because the key names Table 6).\n- JSON cannot keep trailing zeros, so the printed form is given in `printed_values`.\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"Requested items (25 value/boolean keys) assessed; none excluded\",25,25,0,\"Pure extraction task; every requested item is assessed; the unreported HIV p-value is kept as null rather than excluded\"\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json","is_error":false},{"tool_use_id":"toolu_01PkHbqexRwUY7imwjiXXFfx","type":"tool_result","content":"\"\"\"F4-conf-01: extract printed treatment effects/uncertainty from two source extracts.\nReads only data/; writes out/results.json. No randomness.\"\"\"\nimport json, re, os, hashlib\nfrom decimal import Decimal\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nZ = 1.96 # normal critical value for 95% two-sided\n\ndef read(p):\n with open(os.path.join(D, p), encoding=\"utf-8\") as f:\n return f.read()\n\ndef wc_l(p):\n with open(os.path.join(D, p), \"rb\") as f:\n return f.read().count(b\"\\n\")\n\ndcr = read(f\"sources/{DCR_ID}.txt\").split(\"\\n\")\nri = read(f\"sources/{RI_ID}.txt\").split(\"\\n\")\n\ndef table_block(lines, title_prefix):\n i = next(k for k, l in enumerate(lines) if l.startswith(title_prefix))\n j = next(k for k in range(i + 1, len(lines)) if lines[k].startswith(\"Note:\"))\n return [(k + 1, lines[k]) for k in range(i, j) if lines[k].strip()]\n\ndef num(s):\n \"\"\"Parse printed number keeping printed decimals; returns (float, printed str).\"\"\"\n s = s.strip().replace(\"$\", \"\").replace(\",\", \"\").replace(\"*\", \"\").strip(\"()\")\n s = s.replace(\"%\", \"\")\n return (int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)), s\n\ndef dcr_cell(block, row, col):\n header = [c.strip() for c in block[1][1].split(\"|\")]\n ci = header.index(col)\n for idx, (ln, l) in enumerate(block):\n cells = [c.strip() for c in l.split(\"|\")]\n if cells[0] == row:\n raw = cells[ci]\n print(f\"[{DCR_ID} line {ln}] {l}\")\n out = {\"value\": num(raw)[0], \"stars\": raw.count(\"*\"), \"raw\": raw}\n nxt = block[idx + 1] if idx + 1 < len(block) else None\n if nxt and nxt[1].lstrip().startswith(\"| (\"):\n print(f\"[{DCR_ID} line {nxt[0]}] {nxt[1]}\")\n se_raw = [c.strip() for c in nxt[1].split(\"|\")][ci]\n out[\"se\"] = num(se_raw)[0]; out[\"se_raw\"] = se_raw\n return out\n raise KeyError(row)\n\nt6 = table_block(dcr, \"Table 6:\")\nt7 = table_block(dcr, \"Table 7:\")\nfor ln, l in t6[:2] + t7[:2]:\n print(f\"[{DCR_ID} line {ln}] {l}\")\n\na5 = dcr_cell(t6, \"ATT\", \"5 km\"); a3 = dcr_cell(t6, \"ATT\", \"3 km\")\nobs5 = dcr_cell(t6, \"Observations\", \"5 km\")\nnarc3 = dcr_cell(t7, \"Narcotics\", \"3 km\")\nfat1 = dcr_cell(t7, \"Drug-related (fatal)\", \"1 km\")\nhiv5 = dcr_cell(t7, \"HIV\", \"5 km\")\n\n# exact p-value search: any \"p=\" / \"p-value\" in DCR extract tied to HIV?\ndcr_text = \"\\n\".join(dcr)\np_hits = [l for l in dcr if re.search(r\"p\\s*=\\s*0?\\.\\d\", l)]\nhiv_p = None # stars only (none on HIV 5 km); no exact p-value printed\nprint(f\"DCR lines with an exact 'p=' value: {len(p_hits)}\")\n\n# derived CI (Table 6, 5 km); round to 4 dp (one more than the printed 3 dp)\nci_lo = round(a5[\"value\"] - Z * a5[\"se\"], 4)\nci_hi = round(a5[\"value\"] + Z * a5[\"se\"], 4)\n\n# RESEA Table 2\nt2_hdr = next(k for k, l in enumerate(ri) if l.startswith(\"Outcome | Treatment | Control\"))\nheader = [c.strip() for c in ri[t2_hdr].split(\"|\")]\nprint(f\"[{RI_ID} line {t2_hdr+1}] {ri[t2_hdr]}\")\ndef ri_row(label):\n for k in range(t2_hdr + 1, len(ri)):\n cells = [c.strip() for c in ri[k].split(\"|\")]\n if cells[0] == label:\n print(f\"[{RI_ID} line {k+1}] {ri[k]}\")\n return dict(zip(header, cells))\n raise KeyError(label)\nATE = \"Average treatment effect estimate\"\ndef parse_effect(cell):\n est = cell.split(\";\")[0]\n est = re.sub(r\"\\s*(pp|weeks|wks)$\", \"\", est.strip())\n m = re.search(r\"95% CI: \\[(.+?),\\s*(-?[\\d.]+(?:,\\d{3})*(?:\\.\\d+)?)\\]\", cell)\n lo, hi = m.group(1), m.group(2)\n se_printed = bool(re.search(r\"\\bSE\\b|standard error\", cell, re.I))\n return num(est)[0], num(lo)[0], num(hi)[0], se_printed\nw = ri_row(\"Wage\"); r = ri_row(\"Reemployment\"); u = ri_row(\"Weeks on UI\")\nw_ate, w_lo, w_hi, w_se_printed = parse_effect(w[ATE])\nr_ate, _, _, _ = parse_effect(r[ATE])\nu_ate, u_lo, u_hi, _ = parse_effect(u[ATE])\nctrl_mean = num(w[\"Control\"].split(\"(\")[0])[0]\nw_se = round((w_hi - w_lo) / (2 * Z), 2)\n\nS6 = f\"{DCR_ID} | Table 6\"; S7 = f\"{DCR_ID} | Table 7\"; S2 = f\"{RI_ID} | Table 2\"\nloc = {\n \"dcr_att_5km\": f'{S6} | row \"ATT\" | column \"5 km\"',\n \"dcr_att_5km_se\": f'{S6} | row \"ATT\" (standard error line in parentheses) | column \"5 km\"',\n \"dcr_att_3km\": f'{S6} | row \"ATT\" | column \"3 km\"',\n \"dcr_att_3km_se\": f'{S6} | row \"ATT\" (standard error line in parentheses) | column \"3 km\"',\n \"dcr_obs_5km\": f'{S6} | row \"Observations\" | column \"5 km\"',\n \"dcr_narcotics_att_3km\": f'{S7} | row \"Narcotics\" | column \"3 km\"',\n \"dcr_narcotics_att_3km_se\": f'{S7} | row \"Narcotics\" (standard error line in parentheses) | column \"3 km\"',\n \"dcr_fatal_att_1km\": f'{S7} | row \"Drug-related (fatal)\" | column \"1 km\"',\n \"dcr_fatal_att_1km_se\": f'{S7} | row \"Drug-related (fatal)\" (standard error line in parentheses) | column \"1 km\"',\n \"dcr_hiv_att_5km\": f'{S7} | row \"HIV\" | column \"5 km\"',\n \"dcr_hiv_att_5km_se\": f'{S7} | row \"HIV\" (standard error line in parentheses) | column \"5 km\"',\n \"dcr_hiv_att_5km_p_value\": f\"not reported in {DCR_ID} (Table 7 gives only significance stars; the HIV 5 km cell has none)\",\n \"dcr_att_5km_ci_low\": f'{S6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (normal, 1.96)',\n \"dcr_att_5km_ci_high\": f'{S6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (normal, 1.96)',\n \"ri_wage_ate\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\"',\n \"ri_wage_ate_ci_low\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n \"ri_wage_ate_ci_high\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n \"ri_wage_ate_se_derived\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" | derived from printed 95% CI (width / (2 x 1.96)); SE not reported in {RI_ID}',\n \"ri_control_mean_wage\": f'{S2} | row \"Wage\" | column \"Control\"',\n \"ri_reemployment_ate_pp\": f'{S2} | row \"Reemployment\" | column \"Average treatment effect estimate\"',\n \"ri_weeks_ui_ate\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\"',\n \"ri_weeks_ui_ate_ci_low\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n \"ri_weeks_ui_ate_ci_high\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n}\n\nissues = [\n {\"issue\": f\"{DCR_ID}: Table 6 prints SE (0.210) for the 5 km ATT -0.431**, while Appendix Table A19 (CS-DR column) prints the same ATT with SE (0.218).\",\n \"handling\": \"Used Table 6 value (0.210) as the key definition names Table 6; discrepancy reported.\"},\n {\"issue\": f\"{DCR_ID}: Tables 6/7 and Figure 6 state 608 never-treated control regions, the A19 note states 640; the Table 6 observation counts (16,175/16,875/17,250) equal 25 x (640 + 7/35/50 treated regions), i.e. consistent with 640 controls.\",\n \"handling\": \"Observation count reported as printed (17250); inconsistency not resolvable from the extract, reported only.\"},\n {\"issue\": f\"{DCR_ID}: abstract says fatal hospitalizations 'fall by 35%', whereas Table 7 fatal 5 km ATT -0.354 log points implies about -29.8% (Section 6.1 says ~30%); prose also calls HIV estimates 'close to zero' though 3/5 km ATTs are -0.239/-0.340.\",\n \"handling\": \"Prose not used; table values extracted as printed.\"},\n {\"issue\": f\"{RI_ID}: prose reports the weeks-on-UI CI as magnitudes [1.75 weeks, 2.24 weeks]; Table 2 prints [-2.24, -1.75]. Prose also has a rendering defect 'p ¡ 0.001'.\",\n \"handling\": \"Table 2 values used (lower bound -2.24 first).\"},\n]\n\nres = {\n \"task_id\": \"F4-conf-01\",\n \"dcr_att_5km\": a5[\"value\"], \"dcr_att_5km_se\": a5[\"se\"],\n \"dcr_att_3km\": a3[\"value\"], \"dcr_att_3km_se\": a3[\"se\"],\n \"dcr_obs_5km\": int(obs5[\"value\"]),\n \"dcr_narcotics_att_3km\": narc3[\"value\"], \"dcr_narcotics_att_3km_se\": narc3[\"se\"],\n \"dcr_fatal_att_1km\": fat1[\"value\"], \"dcr_fatal_att_1km_se\": fat1[\"se\"],\n \"dcr_hiv_att_5km\": hiv5[\"value\"], \"dcr_hiv_att_5km_se\": hiv5[\"se\"],\n \"dcr_hiv_att_5km_p_value\": hiv_p,\n \"dcr_hiv_att_5km_significant_10pct\": hiv5[\"stars\"] >= 1,\n \"dcr_att_5km_ci_low\": ci_lo, \"dcr_att_5km_ci_high\": ci_hi,\n \"ri_wage_ate\": w_ate, \"ri_wage_ate_ci_low\": w_lo, \"ri_wage_ate_ci_high\": w_hi,\n \"ri_wage_ate_se_reported\": w_se_printed,\n \"ri_wage_ate_se_derived\": w_se,\n \"ri_control_mean_wage\": ctrl_mean,\n \"ri_reemployment_ate_pp\": r_ate,\n \"ri_weeks_ui_ate\": u_ate, \"ri_weeks_ui_ate_ci_low\": u_lo, \"ri_weeks_ui_ate_ci_high\": u_hi,\n \"locators\": loc,\n \"derivation_notes\": {\n \"dcr_att_5km_ci\": \"Normal (Wald) 95% CI: ATT +/- 1.96 x SE using Table 6 printed ATT -0.431 and SE 0.210 (clustered SE, large-sample normal approximation); rounded to 4 decimals.\",\n \"ri_wage_ate_se\": \"SE = (upper - lower) / (2 x 1.96) using Table 2 printed 95% CI [166, 2,141]; assumes a symmetric normal-based CI (printed CI is symmetric around 1,153 to within rounding: 987 vs 988); rounded to 2 decimals. Using 2.0 instead would give 493.75.\"},\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"README.md\": wc_l(\"README.md\"),\n f\"sources/{DCR_ID}.txt\": wc_l(f\"sources/{DCR_ID}.txt\"),\n f\"sources/{RI_ID}.txt\": wc_l(f\"sources/{RI_ID}.txt\")},\n \"n_analysed\": 25,\n \"random_seed\": None,\n}\n# baseline: cruder \"2-sigma\" convention for the two derived quantities (sensitivity reference)\nres[\"baseline_dcr_att_5km_ci_2se\"] = [round(a5[\"value\"] - 2.0 * a5[\"se\"], 4), round(a5[\"value\"] + 2.0 * a5[\"se\"], 4)]\nres[\"baseline_ri_wage_ate_se_2se\"] = round((w_hi - w_lo) / 4.0, 2)\n# printed strings (JSON numbers drop trailing zeros, e.g. 0.210 -> 0.21)\nres[\"printed_values\"] = {\n \"dcr_att_5km\": a5[\"raw\"], \"dcr_att_5km_se\": a5[\"se_raw\"],\n \"dcr_att_3km\": a3[\"raw\"], \"dcr_att_3km_se\": a3[\"se_raw\"],\n \"dcr_obs_5km\": obs5[\"raw\"],\n \"dcr_narcotics_att_3km\": narc3[\"raw\"], \"dcr_narcotics_att_3km_se\": narc3[\"se_raw\"],\n \"dcr_fatal_att_1km\": fat1[\"raw\"], \"dcr_fatal_att_1km_se\": fat1[\"se_raw\"],\n \"dcr_hiv_att_5km\": hiv5[\"raw\"], \"dcr_hiv_att_5km_se\": hiv5[\"se_raw\"],\n \"ri_wage_cell\": w[ATE], \"ri_control_wage_cell\": w[\"Control\"],\n \"ri_reemployment_cell\": r[ATE], \"ri_weeks_ui_cell\": u[ATE],\n}\n# consistency diagnostics (printed only)\nimport math\nzw = w_ate / w_se\nprint(f\"Check: wage ATE z={zw:.3f}, implied two-sided normal p={math.erfc(abs(zw)/math.sqrt(2)):.4f} (printed p=0.022)\")\nprint(f\"Check: DCR 5km |t|={abs(a5['value'])/a5['se']:.3f} (printed **, i.e. p<0.05)\")\nprint(f\"Check: HIV 5km |t|={abs(hiv5['value'])/hiv5['se']:.3f} (no star)\")\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"locators\", \"derivation_notes\", \"data_issues\")}, 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\"ri_control_wage_cell\": \"$33,043 (SD: 36,231)\",\n \"ri_reemployment_cell\": \"1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\",\n \"ri_weeks_ui_cell\": \"-1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\"\n }\n}\nIDENTICAL\n[arxiv-2609.27732v1 line 32] Table 6: Overall Average Treatment Effect on the Treated (ATT)\n[arxiv-2609.27732v1 line 34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1 line 66] Table 7: Overall ATT by Outcome Category and Buffer Zone\n[arxiv-2609.27732v1 line 68] Outcome | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1 line 36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1 line 38] | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1 line 36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1 line 38] | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1 line 46] Observations | 16,175 | 16,875 | 17,250\n[arxiv-2609.27732v1 line 82] Narcotics | 1.186*** | 0.549*** | 0.390**\n[arxiv-2609.27732v1 line 84] | (0.288) | (0.187) | (0.165)\n[arxiv-2609.27732v1 line 86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n[arxiv-2609.27732v1 line 88] | (0.137) | (0.215) | (0.178)\n[arxiv-2609.27732v1 line 78] HIV | 0.063 | -0.239 | -0.340\n[arxiv-2609.27732v1 line 80] | (0.801) | (0.337) | (0.294)\nDCR lines with an exact 'p=' value: 0\n[arxiv-2606.14621v1 line 27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n[arxiv-2606.14621v1 line 28] Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n[arxiv-2606.14621v1 line 29] Reemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n[arxiv-2606.14621v1 line 30] Weeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\nCheck: wage ATE z=2.288, implied two-sided normal p=0.0221 (printed p=0.022)\nCheck: DCR 5km |t|=2.052 (printed **, i.e. p<0.05)\nCheck: HIV 5km |t|=1.156 (no star)\n{\n \"task_id\": \"F4-conf-01\",\n \"dcr_att_5km\": -0.431,\n \"dcr_att_5km_se\": 0.21,\n \"dcr_att_3km\": -0.283,\n \"dcr_att_3km_se\": 0.279,\n \"dcr_obs_5km\": 17250,\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0281","is_error":false}]},"uuid":"047a2425-c139-491d-8294-d83e5b87f887","timestamp":"2026-10-30T05:25:43.232Z","toolUseResult":{"stdout":"0\n \"ri_control_wage_cell\": \"$33,043 (SD: 36,231)\",\n \"ri_reemployment_cell\": \"1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\",\n \"ri_weeks_ui_cell\": \"-1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\"\n }\n}\nIDENTICAL\n[arxiv-2609.27732v1 line 32] Table 6: Overall Average Treatment Effect on the Treated (ATT)\n[arxiv-2609.27732v1 line 34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1 line 66] Table 7: Overall ATT by Outcome Category and Buffer Zone\n[arxiv-2609.27732v1 line 68] Outcome | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1 line 36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1 line 38] | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1 line 36] ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1 line 38] | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1 line 46] Observations | 16,175 | 16,875 | 17,250\n[arxiv-2609.27732v1 line 82] Narcotics | 1.186*** | 0.549*** | 0.390**\n[arxiv-2609.27732v1 line 84] | (0.288) | (0.187) | (0.165)\n[arxiv-2609.27732v1 line 86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n[arxiv-2609.27732v1 line 88] | (0.137) | (0.215) | (0.178)\n[arxiv-2609.27732v1 line 78] HIV | 0.063 | -0.239 | -0.340\n[arxiv-2609.27732v1 line 80] | (0.801) | (0.337) | (0.294)\nDCR lines with an exact 'p=' value: 0\n[arxiv-2606.14621v1 line 27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n[arxiv-2606.14621v1 line 28] Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n[arxiv-2606.14621v1 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The extract does not state the panel length, so this is a plausible inference rather than a fact taken from the source.\",\n \"suggested_fix\": \"Add that the 640 inference assumes 25 periods per region, and that this is not stated in the extract.\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"claims.json C4; report.md Uncertainty\",\n \"issue\": \"The derived 95% CI excludes zero only narrowly (upper bound -0.0194; |t| = 2.05). The report notes that the A19 SE of 0.218 would widen the interval but does not compute it. With 0.218 the upper bound is about -0.004, so the interval still excludes zero, but only just. The caveat could state how marginal this is.\",\n \"suggested_fix\": \"State in the C4 caveat that the exclusion of zero is marginal and depends on the third decimal of the SE: about -0.004 with the A19 SE of 0.218.\"\n }\n ],\n \"summary\": \"Re-running analysis.py in a scratch copy reproduces results.json byte-for-byte as JSON. All 25 required value and boolean keys match the source extracts as printed: Table 6, Table 7, and RESEA Table 2 (not Table 3). Signs are correct and the CI bound order is correct. The script reads only data/ and prints every matched source line. Both derived quantities are arithmetically correct and the convention is stated: CI [-0.8426, -0.0194] with 1.96; SE 503.83 with 1.96. A 2.0 sensitivity check is given. The unreported HIV p-value is correctly null with a 'not reported' locator, and the RESEA SE is correctly flagged as not reported. Locators cover all 23 non-boolean keys, and the n_input_rows counts match wc -l. The source inconsistencies (Table 6 vs A19 SE, 608 vs 640 controls, abstract prose vs tables) are reported honestly. Only minor presentation issues remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"}}]}}47{"type":"api-request","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","id":"45480684-e30b-4211-82ca-93a30f0c5a5b","timestamp":"2026-10-30T05:25:58.175Z","version":"2.1.314","querySource":"sdk","shapeHash":"7e0cdcf9a5ddfb2d9035886ed1c7b5b382185a823b059fe9dce44293c7637ce4","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"12581512-dc51-4cc7-94b8-42d0ce6f14c6","keep":12,"tail":["e2837ec421a3ff6f60ae96472862f540304b8adf92eaff33d5a28af81aaf9c13","07f7e3dceecac1b13317683cac7879e71a240e9d9b5046fbaafd192ee9512e5c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"fab52c42dc7039dbff1ff3bebf24caf13c5a98d467dfaa2b547a981889aa8bf7"}48{"parentUuid":"ae95417f-0efb-4471-97cc-0a8ea50adf64","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsCQavJoACFkMHv7RBR","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1488,"cache_read_input_tokens":33715,"output_tokens":669,"output_tokens_details":{"thinking_tokens":10},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1488},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":5744,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsCQKYZvEw9AnSGr1Vd","requestRef":"45480684-e30b-4211-82ca-93a30f0c5a5b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0269737a-3509-45d0-b795-5ca91a6e6a6f","timestamp":"2026-10-30T05:26:05.451Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"0269737a-3509-45d0-b795-5ca91a6e6a6f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsCQavJoACFkMHv7RBR","type":"message","role":"assistant","content":[{"type":"text","text":"My verdict is **accept**. I found no critical or major issues, only three minor presentation points, and wrote the review to `out/review/review.json`.\n\nI re-ran `out/analysis.py` in a scratch copy and it regenerated `out/results.json` exactly. All 25 required values match the two source extracts as printed, including signs and the order of the confidence-interval bounds. The script reads only `data/` and prints each source line it matched. It takes the RESEA numbers from Table 2, as the task requires, not from the duplicate Table 3.\n\nBoth derived quantities are calculated correctly and use the 1.96 convention, which is stated in the notes:\n- **5 km confidence interval:** [-0.8426, -0.0194].\n- **Wage standard error:** 503.83 USD per year. A cross-check gives p ≈ 0.0221, which matches the printed p = 0.022.\n\nThe deliverables also show the result under a 2.0 convention for comparison. The HIV 5 km p-value is not printed in the source, and the deliverables correctly report it as null and say so in its locator. They also correctly state that the RESEA paper prints no standard error for the wage effect. Every non-boolean key has a locator, and the line counts match `wc -l`. The report honestly lists the inconsistencies inside the sources: the 0.210 vs 0.218 standard error, the 608 vs 640 control regions, and abstract prose that disagrees with the tables.\n\nThe three minor points:\n1. **Trailing zeros:** JSON stores 0.210 as 0.21 and -0.340 as -0.34. The values are the same, and the verbatim strings are kept under `printed_values`.\n2. **The \"640 controls\" inference:** it assumes a balanced 25-period panel, which the extract does not state.\n3. **Claim C4:** the derived interval only narrowly excludes zero. With the appendix standard error of 0.218 instead of 0.210, the upper bound would be about -0.004, so the caveat should say how marginal this is."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1488,"cache_read_input_tokens":33715,"output_tokens":669,"output_tokens_details":{"thinking_tokens":10},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1488},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsCQKYZvEw9AnSGr1Vd","requestRef":"45480684-e30b-4211-82ca-93a30f0c5a5b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6436df1d-ecc2-4d6a-ab38-8e302580018d","timestamp":"2026-10-30T05:26:05.454Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","version":"2.1.314","gitBranch":"HEAD"}50{"type":"last-prompt","lastPrompt":"Review","leafUuid":"6436df1d-ecc2-4d6a-ab38-8e302580018d","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a"}51{"type":"atis-latch","atis":"","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a"}52{"type":"cost-state","sessionId":"9c598de0-1c0e-4402-bc1e-eaf43ad73b1a","totalCostUSD":0.25479779999999996,"totalAPIDuration":39292,"totalAPIDurationWithoutRetries":39286,"totalToolDuration":461,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":39911,"startTime":1793337925551,"modelUsage":{"claude-opus-5-5":{"inputTokens":16,"outputTokens":3140,"thinkingTokens":676,"cacheReadInputTokens":117194,"cacheCreationInputTokens":33699,"cacheCreationEphemeral5mInputTokens":33699,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.25479779999999996}},"hasUnknownModelCost":false}53