Reply to: On meta-analytic models and the effect of hydroxychloroquine use in COVID-19
Bibliographic record
Abstract
We appreciate the comments by Pasquier on our meta-analysis 1 . Pasquier states that the “significant increase of mortality associated with HCQ (OR, 1.11, 95%CI, 1.02–1.20, p = 0.02) … is in contrast with other meta-analyses based on similar sets of trials, which reported wider confidence intervals 2 , 3 , 4 , 5 .” and that “The difference between Axfors et al. and other meta-analyses originates mostly from the meta-analytic model used”, rather than the fact that we included more studies.” However, while we agree that different meta-analytic model choices may give non-identical results (a well-known fact that affects any meta-analysis) 6 our conclusions are not materially altered by using the method Pasquier suggests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.039 | 0.042 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".