Using Metaanalysis to Evaluate Evidence: Practical Tips and Traps
Bibliographic record
Abstract
Although practising evidence-based medicine is the goal of most physicians, it can be a real challenge to sift through the vast body of data to determine the best strategies. Most clinical guidelines regard replicated randomized controlled trials (RCTs), metaanalyses, and systematic reviews as the highest level of evidence to support treatment recommendations. High-quality metaanalyses can overcome many of the drawbacks of individual RCTs and qualitative reviews. They can reduce bias, provide adequate power to demonstrate real differences in outcomes, and resolve the results of inconsistent studies. This paper focuses on basic principles and terms used in metaanalysis, so that clinicians can appropriately evaluate and use their results to guide treatment decisions.
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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 | 0.519 | 0.685 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.008 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.020 | 0.045 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.017 | 0.056 |
| Insufficient payload (model declined to judge) | 0.004 | 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".