Tips for Improving the Writing and Reporting Quality of Systematic, Scoping, and Narrative Reviews
Bibliographic record
Abstract
The evidence base in health professions education continues to accumulate at an unprecedented rate. Summaries of evidence in the form of scoping, systematic and narrative reviews are also increasingly common. Unfortunately, many submissions go unpublished and for reasons that may be irreversible post-peer review. The goal of this commentary is to offer insights to review authors for improving the likelihood of publication success. These tips will not guarantee success; however, insights address common errors authors make along the continuum of review production that result in either requests for major revision or rejection.
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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 | 0.672 | 0.923 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.038 | 0.029 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.035 | 0.046 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.030 | 0.054 |
| Insufficient payload (model declined to judge) | 0.016 | 0.018 |
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".