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

# Question

A literature review makes six assertions (A1–A6). Each one cites either Armendariz (2026, arXiv:2609.27732v1, drug consumption rooms, DCR) or Li, Pearson-Merkowitz & Yokum (2026, arXiv:2606.14621v1, RESEA). Using only the cited extract, I classify each assertion as supported, not_supported or not_assessable. Each verdict comes with one locator and a one-sentence reason (verdicts, locators, reasons).

# Data and exclusions

I read four inputs; their line and item counts are in n_input_rows. They are assertions.json (6 items), README.md (14 lines) and the two extracts (134 and 58 lines). The SHA-256 hashes of both extracts match the values in the README (out/state/data_audit.md). No assertion was excluded (out/exclusions.csv: 6 − 0 = 6), and all six were assessed (n_analysed = 6). The task was completed in full (feasibility = "complete").

I found these problems in the sources (data_issues):

In the DCR paper, Table A19 prints the CS-DR 5 km ATT with SE 0.218, while Table 6 gives SE 0.210 for the same specification.
The Table A19 note says there are 640 control regions; everywhere else the paper says 608.
The abstract says fatal hospitalisations fall by 35%, but Table 7's coefficient of −0.354 implies −29.8%, which matches the "≈30%" in Section 6.1.
The Figure 7 note mentions four sub-categories, but Table 7 has five.
The RESEA results text prints confidence intervals for reductions as unsigned magnitudes, and has a rendering defect ("p ¡ 0.001").

# Method

out/analysis.py locates the deciding table rows in the extracts by matching strings and prints those lines. It then applies the conventions set by the task:

percentage change = 100·(exp(b) − 1);
95% CI = b ± 1.96·SE, with ±2.0·SE as a check;
for significance, the printed stars.

The verdict rules are written explicitly in the script and involve no randomness (random_seed = null). As a simple baseline, I also checked whether every decimal number in an assertion appears anywhere in the cited extract (baseline_verdicts_number_match). This baseline agrees with the final verdicts on 4 of 6 assertions (baseline_agreement_with_final). It gets A2 wrong because it ignores the sign, and A5 wrong because that assertion contains no numbers.

# Results

id	verdict	locator	decisive element
A1	supported	DCR Table 6, row "ATT", col "5 km"	−0.431 (0.210); means p<0.05; the printed % change is −35.0%, and 100(exp(−0.431)−1) = −35.01% (`derived_quantities.A1_pct_change_5km`)
A2	not_supported	DCR Table 7, row "HIV", col "1 km"	the printed ATT is +0.063 (SE 0.801), not −0.063, so the claimed fall is contradicted
A3	not_supported	DCR Table 6, row "% change", col "3 km"	−0.283 log points is −24.7% (printed), or −24.65% when computed (`derived_quantities.A3_pct_change_3km`), not 28.3%
A4	not_supported	DCR Table 7, row "Hepatitis", col "5 km"	−0.406 (0.250) gives a 95% CI of [−0.896, 0.084], which includes zero (`derived_quantities.A4_ci95_1.96`); the asserted interval equals ±1 SE (`derived_quantities.A4_ci_pm_1se`)
A5	not_assessable	not reported in arxiv-2609.27732v1	the extract has no results by sex (no line mentions men, women, sex or gender)
A6	supported	RESEA Table 2, row "Weeks on UI", col "Average treatment effect estimate"	−1.99 weeks, CI [−2.24, −1.75], control mean 17.3; −1.99/17.3 = −11.50% (`derived_quantities.A6_pct_of_control_mean`)

Counts: n_supported = 2, n_not_supported = 3, n_not_assessable = 1.

# Uncertainty

The verdicts are deterministic judgments, not statistical estimates. Here is which ones depend on a convention, and whether a reasonable alternative would change them:

A1 depends on the significance threshold. With Table 6's SE of 0.210, |t| = 2.05, which passes both the 1.96 and the 2.0 critical values; ** is printed as well, so the verdict is robust. If a reader used Table A19's SE of 0.218 for the same estimate, |t| = 1.98 would pass 1.96 but not 2.0. The paper still prints ** there, and the assertion quotes SE 0.210, which matches Table 6. The % change does not depend on any convention, because −35.0% is printed.
A3 depends on the log-to-percent conversion. Under the exp convention and in the source's own printed row, the change is −24.7%. The figure 28.3% would only come from treating log points as percent, which is an approximation the task rules out and which is also inaccurate at this size. The verdict is robust.
A4 depends on the critical value. With 1.96 the interval is [−0.896, 0.084]; with 2.0 it is [−0.906, 0.094] (derived_quantities.A4_ci95_2.0). Both include zero, and no significance star is printed, so the verdict is robust. The asserted interval matches only a ±1 SE (≈68%) band.
A2, A5 and A6 depend on no convention. A6's 11.5% is printed in the source and also follows arithmetically.

# Limitations and open issues

The verdicts rely only on the extracts. For example, A5 might be assessable from omitted sections of the full paper, but those are inadmissible here.
The DCR paper is internally inconsistent in places (the SE in A19, the control-region count, the abstract's fatal-outcome percentage). None of these changes a verdict, but they are reported in data_issues.
For A6, "selection into RESEA" is taken to match the paper's ITT/ATE of RESEA selection.

spb/wp29_uqo