Topic duplication and research waste at the ‘Overviews of systematic reviews’ level: Survey of overlapping overviews
Bibliographic record
Abstract
Abstract Background Multiple overviews of systematic reviews conducted on the same topic (“overlapping overviews”) represent a waste of research resources and can confuse or mislead clinicians and policymakers. We aimed to assess the frequency and characteristics of published overviews addressing the same clinical question or topic. Methods We used MEDLINE, Epistemonikos and Cochrane databases to locate overviews that: focused on synthesising reviews; conducted systematic searches; had a methods section; and examined a health intervention or clinical treatment. We then determined which overviews addressed the same or overlapping populations/settings, interventions, and outcomes [PIO]). Overlap in topic was defined as: duplication of PIO elements, not representing an update of a previous overview, and not a replication for quality purposes. Results Of 541 overviews located (2000–2018), 178 (33%) overlapped with another overview addressing a similar PIO. The topics of overlapping overviews fell within 13 WHO ICD-10 medical classifications, and there were 65 overlapping topics in total. The most prevalent topic with overlap across 7 overviews was smoking cessation (pharmacologic and non-pharmacologic interventions). Five overlapping overviews related to acupuncture for pain, 5 addressed cannabinoids for pain and symptoms, and 5 addressed exercise for bone and muscle health. For 15/65 (22%) of these topics, one author was involved in at least two of the overlapping overviews. Conclusions We found significant duplication and unnecessary overlap across overviews. To avoid waste and redundancy, protocols of overviews should be registered in a targeted database, and overviews should cite other studies on similar topic with a rationale.
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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 | 0.476 | 0.782 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.081 | 0.074 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.004 | 0.003 |
| 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".