Optimal strategies to consider when peer reviewing a systematic review and meta-analysis
Bibliographic record
Abstract
Systematic reviews are popular. A recent estimate indicates that 11 new systematic reviews are published daily. Nevertheless, evidence indicates that the quality of reporting of systematic reviews is not optimal. One likely reason is that the authors' reports have received inadequate peer review. There are now many different types of systematic reviews and peer reviewing them can be enhanced by using a reporting guideline to supplement whatever template the journal editors have asked you, as a peer reviewer, to use. Additionally, keeping up with the current literature, whether as a content expert or being aware of advances in systematic review methods is likely be make for a more comprehensive and effective peer review. Providing a brief summary of what the systematic review has reported is an important first step in the peer review process (and not performed frequently enough). At its core, it provides the authors with some sense of what the peer reviewer believes was performed (Methods) and found (Results). Importantly, it also provides clarity regarding any potential problems in the methods, including statistical approaches for meta-analysis, results, and interpretation of the systematic review, for which the peer reviewer can seek explanations from the authors; these clarifications are best presented as questions to the authors.
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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 | 0.629 | 0.879 |
| Meta-epidemiology (narrow) | 0.007 | 0.010 |
| Meta-epidemiology (broad) | 0.018 | 0.012 |
| Bibliometrics | 0.031 | 0.019 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.033 | 0.039 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.026 | 0.027 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".