Development and application of an outcome-centric approach for conducting overviews of reviews
Bibliographic record
Abstract
There are gaps in current guidance concerning how to conduct overviews of systematic reviews in an outcome-centric manner. Herein we summarize the methods and lessons learned from conducting 4 outcome-centric overviews to help inform the Canadian 24-Hour Movement Guidelines for Adults aged 18–64 years and Adults aged 65 years or older on the topics of resistance training, balance and functional training, sedentary behaviour, sleep duration. We defined “critical” and “important” outcomes a priori. We used AMSTAR 2 to assess review quality and sought 1 systematic review per outcome. If multiple reviews were required to address subgroups for an outcome, we calculated the corrected covered area (CCA) to quantify overlap. We report our methodology in a PRISMA table. Across the 4 overviews, authors reviewed 1110 full texts; 45 were retained (low to high quality per AMSTAR 2), representing 950 primary studies, enrolling over 5 385 500 participants. Of 46 outcomes, we identified data for 35. Nineteen outcomes required >1 review (CCA range: 0% to 71.4%). Our outcome-centric overviews addressed unique aspects of overviews, including selection and quality assessment of included reviews, and overlap. Lessons learned included consistent application of methodological principles to minimize bias and optimize reporting transparency. Novelty Overviews of reviews synthesize systematic reviews in a rigorous and transparent manner. Outcome-centric systematic reviews assess the quality of evidence for primary studies contributing to an outcome. This manuscript describes the development and application of extending the concept of outcome-centric systematic reviews to the design and conduct of outcome-centric overviews.
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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 | 0.718 | 0.829 |
| Meta-epidemiology (narrow) | 0.008 | 0.011 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.079 | 0.053 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.014 | 0.026 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".