More statistical analysis is needed for the meta‐analysis of genetic association studies: authors’ reply
Bibliographic record
Abstract
Sirs, We agree with Chen et al.1 that detailed statistical analysis may be useful in general for the meta-analysis of genetic association studies if substantial heterogeneity was observed. However, application of the procedures cited by Chen et al. would not have been very meaningful in our meta-analyses2 given the small numbers of available studies. We therefore addressed the important issues raised by Chen et al. in a way that appears more adequate given the number of available studies. In order to explore the potential origin of heterogeneity, we carefully performed stratified analyses according to ethnicity and subtype of gastric cancer. As a kind of sensitivity analysis, we excluded one study showing deviation from Hardy-Weinberg Equilibrium. This exclusion did not materially affect the results. (Page 572, para 1, lines 18-22) We specifically addressed the need of further studies in the conclusion. Declaration of personal and funding interests: None.
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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 | 0.026 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.052 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".