Journal recommended guidelines for systematic review and meta‐analyses
Bibliographic record
Abstract
Systematic reviews and meta-analyses aggregate research findings across studies and populations, making them a valuable form of research evidence. Over the past decade, studies in medical education using these methods have increased by 630%. However, many manuscripts are not publication-ready due to inadequate planning and insufficient analyses. These guidelines aim to improve the clarity and comprehensiveness of reporting methodologies and outcomes, ensuring high quality and comparability. They align with existing standards like PRISMA, providing examples and best practices. Adhering to these guidelines is crucial for publication consideration in Anatomical Sciences Education.
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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 | 0.142 | 0.513 |
| Meta-epidemiology (narrow) | 0.005 | 0.009 |
| Meta-epidemiology (broad) | 0.015 | 0.019 |
| Bibliometrics | 0.036 | 0.038 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.017 | 0.007 |
| Research integrity | 0.023 | 0.020 |
| Insufficient payload (model declined to judge) | 0.098 | 0.058 |
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".