Issues in conducting and disseminating brief reviews of evidence
Bibliographic record
Abstract
A brief review of evidence is limited in time and/or scope compared to a comprehensive review. However, brief reviews are important not only in meeting the needs of policy makers and practitioners, but also in providing students and researchers with an overview of the evidence. In this paper we summarise and evaluate alternative methods for brief reviews, including: using strict inclusion criteria; reviewing only a sample of evidence and eliminating or reducing steps in the review process. We examine a sample of brief reviews and found that the majority did not meet the methodological standards of comprehensive reviews. We conclude by recommending some methodological standards for brief reviews.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.904 | 0.968 |
| Meta-epidemiology (narrow) | 0.005 | 0.010 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.034 | 0.043 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.037 | 0.036 |
| Open science | 0.017 | 0.018 |
| Research integrity | 0.028 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".