Automation tools to support undertaking scoping reviews
Bibliographic record
Abstract
OBJECTIVE: This paper describes several automation tools and software that can be considered during evidence synthesis projects and provides guidance for their integration in the conduct of scoping reviews. STUDY DESIGN AND SETTING: The guidance presented in this work is adapted from the results of a scoping review and consultations with the JBI Scoping Review Methodology group. RESULTS: This paper describes several reliable, validated automation tools and software that can be used to enhance the conduct of scoping reviews. Developments in the automation of systematic reviews, and more recently scoping reviews, are continuously evolving. We detail several helpful tools in order of the key steps recommended by the JBI's methodological guidance for undertaking scoping reviews including team establishment, protocol development, searching, de-duplication, screening titles and abstracts, data extraction, data charting, and report writing. While we include several reliable tools and software that can be used for the automation of scoping reviews, there are some limitations to the tools mentioned. For example, some are available in English only and their lack of integration with other tools results in limited interoperability. CONCLUSION: This paper highlighted several useful automation tools and software programs to use in undertaking each step of a scoping review. This guidance has the potential to inform collaborative efforts aiming at the development of evidence informed, integrated automation tools and software packages for enhancing the conduct of high-quality scoping reviews.
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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 | 0.310 | 0.633 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.043 | 0.028 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.040 |
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".