Con: Meta-analysis: some key limitations and potential solutions
Bibliographic record
Abstract
Meta-analysis, a statistical combination of results of several trials to produce a summary effect, has been subject to criticism in the past, mainly for the reasons of poor quality of included studies, heterogeneity between studies meta-analyzed and failing to address publication bias. These limitations can cause the results to be misleading, which is important if policy and practice decisions are based on systematic reviews and meta-analyses. We elaborate on these limitations and illustrate them with examples from the nephrology literature. Finally, we present some potential solutions, notably, education in meta-analysis for evidence producers and consumers as well as the use of individual patient data for meta-analyses.
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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 | 0.313 | 0.679 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".