Critical appraisal of meta-analyses: an introductory guide for the practicing surgeon
Bibliographic record
Abstract
Meta-analyses are an essential tool of clinical research. Meta-analyses of individual randomized controlled trials frequently constitute the highest possible level of scientific evidence for a given research question and allow surgeons to rapidly gain a comprehensive understanding of an important clinical issue. Moreover, meta-analyses often serve as cornerstones for evidence-based surgery, treatment guidelines, and knowledge transfer. Given the importance of meta-analyses to the medical (and surgical) knowledge base, it is of cardinal importance that surgeons have a basic grasp of the principles that guide a high-quality meta-analysis, and be able to weigh objectively the advantages and potential pitfalls of this clinical research tool. Unfortunately, surgeons are often ill-prepared to successfully conduct, critically appraise, and correctly interpret meta-analyses. The objective of this educational review is to provide surgeons with a brief introductory overview of the knowledge and skills required for understanding and critically appraising surgical meta-analyses as well as assessing their implications for their own surgical practice.
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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 | 0.055 | 0.177 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.016 |
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".