# F4-conf-02 — Are the six assertions supported by the sources they cite?

# Question

For six assertions in a draft literature review, decide from the cited extract alone whether each is supported, not_supported or not_assessable, and give a locator and a one-sentence reason for each (keys verdicts, locators, reasons).

# Data and exclusions

Inputs: data/assertions.json (6 assertions), the extracts data/sources/arxiv-2609.27732v1.txt (Armendariz 2026, Swiss drug consumption rooms; 134 lines) and data/sources/arxiv-2606.14621v1.txt (Li, Pearson-Merkowitz & Yokum 2026, Rhode Island RESEA; 58 lines), and data/README.md (key n_input_rows). The SHA-256 hashes of the extracts match the README. No assertion was excluded: all 6 were assessed (n_analysed = 6; out/exclusions.csv). Internal inconsistencies found in the sources are listed in data_issues. None of them affects a verdict.

# Method

out/analysis.py finds the deciding table rows in the extracts, prints them, parses the printed values and applies the verdict rules from TASK.md. Percentage changes are computed as 100·(exp(b)−1) and 95% CIs as b ± 1.96·SE (2.0 as an alternative). An element counts as supported only if the printed number matches it, or follows from it arithmetically. For A5 a regex search for any sex/gender breakdown was run on the cited extract. Before the real analysis, a naive baseline that calls every assertion "supported" was recorded (baseline_naive_all_supported_verdicts). No randomness is used (random_seed = null).

# Results

Counts: 2 supported, 3 not supported and 1 not assessable (n_supported, n_not_supported, n_not_assessable).

id	verdict (`verdicts`)	locator (`locators`)	decisive element (`reasons`, `derived`)
A1	supported	arxiv-2609.27732v1, Table 6, ATT / % change, 5 km	Printed ATT is −0.431** with SE (0.210) and change −35.0%. 100(exp(−0.431)−1) = −35.01% (`derived.A1_pct_from_log`); \|z\| = 2.05 (`derived.A1_z`).
A2	not_supported	Table 7, HIV, 1 km	The printed value is +0.063, not −0.063, so the sign and the claimed fall are wrong.
A3	not_supported	Table 6, % change, 3 km	The implied change is −24.7% as printed (−24.65% computed, `derived.A3_pct_from_log`), not 28.3%. The assertion treats log points as percent.
A4	not_supported	Table 7, Hepatitis, 5 km	−0.406 ± 1.96×0.250 = [−0.896, 0.084] (`derived.A4_ci95_1p96`), which includes zero, and the estimate carries no stars. The claimed [−0.66, −0.16] equals ±1 SE (`derived.A4_ci_pm1se`).
A5	not_assessable	not reported in arxiv-2609.27732v1	The extract has no estimates by sex (0 matching lines, `derived.A5_sex_term_hits`).
A6	supported	arxiv-2606.14621v1, Table 2, Weeks on UI, ATE column	Printed ATE is −1.99 weeks with 95% CI [−2.24, −1.75] and a control mean of 17.3. 1.99/17.3 = 11.50% (`derived.A6_relative_pct`), which matches the text.

The naive baseline agrees with the final verdicts on only 2 of the 6 assertions (A1 and A6).

# Uncertainty

These verdicts are categorical, so no sampling uncertainty applies. Three verdicts depend on a convention:

A1 (critical value and SE source). The 5% significance claim rests on the printed stars, and |z| = 2.05 > 1.96 confirms it. With a critical value of 2.0 the result still holds (2.05 > 2.0). Appendix Table A19 prints SE 0.218 for the same estimate. With that SE, |z| = 1.98 (derived.A1_z_with_A19_se_0218): still significant at 1.96, but just short of 2.0. A19 also prints ** for it, and the assertion quotes the Table 6 SE, so the verdict stays supported. This is the most convention-sensitive verdict.
A3 (log-to-percent conversion). Under the prescribed exp conversion the change is −24.6% to −24.7%. Only by reading log points directly as percent would one get 28.3%, and the printed "% change" row rules that out. The verdict does not change.
A4 (critical value). The CI includes zero with 1.96 ([−0.896, 0.084]) and with 2.0 ([−0.906, 0.094], derived.A4_ci95_2p0). The verdict does not change.
A2, A5 and A6 need no convention. They rest on printed values only, plus the simple ratio for A6.

# Limitations and open issues

The verdicts rely only on the extracts. For A5, a sex-specific analysis may exist in parts of the paper that were omitted from the extract, but by rule it is not available here.
The DCR source contradicts itself in several places (data_issues): the SE is 0.210 in Table 6 but 0.218 in Table A19; the control-region count is 608 in the tables but 640 in the A19 note; the abstract says fatal hospitalisations fall by 35%, while Table 7 implies −29.8%; and the 3 km change is printed as −24.7% while −24.65% is computed. The RESEA text gives CIs as positive magnitudes for a reduction and contains the rendering defect "p ¡ 0.001". Tables were treated as authoritative. None of these issues changes a verdict.
The task is complete (feasibility = "complete").

spb/wp29_uqo