Enhancing the usability of systematic reviews by improving the consideration and description of interventions
Bibliographic record
Abstract
The importance of adequate intervention descriptions in minimising research waste and improving research usability and reproducibility has gained attention in the past few years. Nearly all focus to date has been on intervention reporting in randomised trials. Yet clinicians are encouraged to use systematic reviews, whenever available, rather than single trials to inform their practice. This article explores the problem and implications of incomplete intervention details during the planning, conduct, and reporting of systematic reviews and makes recommendations for review authors, peer reviewers, and journal editors
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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 | 0.892 | 0.969 |
| Meta-epidemiology (narrow) | 0.009 | 0.012 |
| Meta-epidemiology (broad) | 0.027 | 0.018 |
| Bibliometrics | 0.058 | 0.041 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.031 | 0.043 |
| Open science | 0.012 | 0.024 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".