Meta-analysis: A practical decision making tool for surgeons
Bibliographic record
Abstract
BACKGROUND: The exponential rise in published medical research on a yearly basis demands a method to summarise best evidence towards its application to patient care in clinical practice. A robust meta-analysis is a valid tool. It is often considered to be a simple process of pooling results from different studies. This is not true. It appears that surgeons lack a reference guide to help them conduct and appraise a meta-analysis. METHODS: This paper provides a structural framework to perform a meta-analysis. It guides the surgeon on a journey from identification of the correct clinical question to data analysis and through to producing a structured report. Statistical methods are discussed briefly as most commercial software calculates most results in the background. An example of a recent meta-analysis is given. However, important caveats are mentioned as there are limitations of the meta-analytical technique. CONCLUSION: Whereas meta-analyses of homogeneous studies are the highest form of evidence, poorly conducted meta-analyses create confusion and serve to harm the patient. Surgeons practising their art in an era of evidence-based surgery need to understand the principles of meta-analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.236 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.032 | 0.101 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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; both teacher heads 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".