Large scoping reviews: managing volume and potential chaos in a pool of evidence sources
Bibliographic record
Abstract
Scoping reviews can identify a large number of evidence sources. This commentary describes and provides guidance on planning, conducting, and reporting large scoping reviews. This guidance is informed by experts in scoping review methodology, including JBI (formerly Joanna Briggs Institute) Scoping Review Methodology group members, who have also conducted and reported large scoping reviews. We propose a working definition for large scoping reviews that includes approximately 100 sources of evidence but must also consider the volume of data to be extracted, the complexity of the analyses, and purpose. We pose 6 core questions for scoping review authors to consider when planning, developing, conducting, and reporting large scoping reviews. By considering and addressing these questions, scoping review authors might better streamline and manage the conduct and reporting of large scoping reviews from the planning to publishing stage.
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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 | 0.732 | 0.893 |
| Meta-epidemiology (narrow) | 0.005 | 0.013 |
| Meta-epidemiology (broad) | 0.017 | 0.010 |
| Bibliometrics | 0.076 | 0.060 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.066 | 0.072 |
| Open science | 0.015 | 0.051 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".