Only half of the authors of overviews of exercise-related interventions use some strategy to manage overlapping primary studies—a metaresearch study
Bibliographic record
Abstract
OBJECTIVES: The conduct of systematic reviews (SRs) and overviews share several similarities. However, because the unit of analysis for overviews is the SRs, there are some unique challenges. One of the most critical issues to manage when conducting an overview is the overlap of data across the primary studies included in the SRs. This metaresearch study aimed to describe the frequency of strategies to manage the overlap in overviews of exercise-related interventions. STUDY DESIGN AND SETTING: A systematic search in MEDLINE (Ovid), Embase (Ovid), Cochrane Library, Epistemonikos, and other sources was conducted from inception to June 2022. We included overviews of SRs that considered primary studies and evaluated the effectiveness of exercise-related interventions for any health condition. The overviews were screened by two authors independently, and the extraction was performed by one author and checked by a second. We found 353 overviews published between 2005 and 2022 that met the inclusion criteria. RESULTS: One hundred and sixty-four overviews (46%) used at least one strategy to visualize, quantify, or resolve overlap, with a matrix (32/164; 20%), absolute frequency (34/164; 21%), and authors' algorithms (24/164; 15%) being the most used methods, respectively. From 2016 onwards, there has been a trend toward increasing the use of some strategies to manage overlap. Of the 108 overviews that used some strategy to resolve the overlap, ie, avoiding double or multiple counting of primary study data, 79 (73%) succeeded. In overviews where no strategies to manage overlap were reported (n = 189/353; 54%), 16 overview authors (8%) recognized this as a study limitation. CONCLUSION: Although there is a trend toward increasing its use, only half of the authors of the overviews of exercise-related interventions used a strategy to visualize, quantify, or resolve overlap in the primary studies' data. In the future, authors should report such strategies to communicate more valid results.
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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 | 0.207 | 0.525 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".