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Does adding a previously unextracted public registry outcome change the selected-trial evidence for ivermectin in acute outpatient COVID-19? This revised aggregate-data audit incorporates COVID-OUT's reported hospitalization/death endpoint at the common follow-up window, expanding the primary analysis to 7714 records and 98 events. The exploratory pooled risk ratio is 1.107 (95% confidence interval 0.577 to 2.123). All 550 extensions compatible with TOGETHER's separate hospitalization and death margins also include no difference. The newly retrieved appendix does not identify their overlap. A separate denominator sensitivity shows why sparse observed events cannot settle the treatment effect when outcome coverage differs. This is a correction of a targeted audit, not a comprehensive meta-analysis, proof of no effect, or treatment recommendation.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: Conditional on the frozen public trial tables, registry extraction and exploratory estimators, adding COVID-OUT's reported day-28 hospitalization/death endpoint yields 7,714 analysis records and pooled risk ratio 1.107 (95% modified Hartung-Knapp interval 0.577 to 2.123), all 550 compatible TOGETHER hospitalization/death union extensions include no difference, and assignments conditional on nesting COVID-OUT's endpoint denominators within its published modified-intention-to-treat cohort span trial risk ratios 0.083 to 7.766, establishing neither benefit nor equivalence.

Published aggregate follow-up tables can support a transparent sensitivity calculation without reconstructing participant records or fitting the original trial model. We examined 6 long-term recovery rows from PRINCIPLE, keeping the primary and larger sensitivity cohorts separate. For the primary six-month row, the respondent-only treatment-minus-control recovery difference is 3.071 percentage points. Unrestricted binary completion of unavailable responses gives a range from -16.54 to 21.764 percentage points, whereas assigning both arms any common missing-response recovery rate gives a positive range from 2.329 to 2.896 percentage points. These are conditional bounds, not confidence intervals. They quantify how assumptions affect a crude contrast and do not identify a clinical treatment effect.

• Published.Published: In six published PRINCIPLE long-term recovery rows, unrestricted binary completion bounds permit either sign of the crude recovery contrast, every common missing-response recovery rate preserves a positive contrast, and at six months in the primary cohort a missing-treatment recovery rate 13.206 percentage points below the respondent-control anchor erases that contrast, conditional on the stated completion model.

Can unknown overlap between adverse-event terms change the direction of published composite-event estimates? We reconstructed the frozen registry comparisons and calculated every possible range of distinct affected participants consistent with the selected term counts. For adult serious suicidal behaviour, the resulting Mantel–Haenszel risk-ratio bounds are 1.712 to 2.1962; for adult serious suicidality, 1.2154 to 1.8476. The corresponding pediatric bounds are 1.0045 to 1.4773 and 1.8144 to 2.8091. All remain above the null. Unknown overlap therefore changes the magnitude but cannot reverse these fixed-data point estimates under the source's cohort and outcome definitions. These are identification bounds, not confidence intervals or estimates of causal treatment effects.

• Published, Reproduced.Published, Reproduced: In the frozen antidepressant registry dataset, conditional on source cohort selection, denominators, term coding, and absent-term imputation, sharp Mantel-Haenszel risk-ratio bounds relative to selected within-group event-count marginals are 1.7120261533 to 2.1961879882 for adult serious suicidal behaviour, 1.2154409158 to 1.8475901940 for adult serious suicidality, 1.0045047792 to 1.4773331886 for pediatric serious behaviour, and 1.8144436696 to 2.8090979871 for pediatric serious suicidality. Thus unknown participant overlap alone cannot reverse the direction of these fixed-data point estimates; these are not confidence intervals or causal treatment effects.