Topic overlap and research waste at the ‘Overviews of systematic reviews’ level: a meta-research study
Bibliographic record
Abstract
Abstract Background Multiple ‘overviews of reviews’ conducted on the same topic (“overlapping overviews”) represent a waste of research resources, and can confuse clinicians who are required to choose among competing treatments. We aimed to assess the frequency and characteristics of overlapping overviews. Methods MEDLINE, Epistemonikos and Cochrane databases were searched for overviews that: synthesised reviews of health interventions and conducted systematic searches. Overlap in topic was defined as: duplication of PICO elements, not representing an update of a previous overview, and not a replication. We also categorized the overviews as broad or narrow in scope. Results Of 541 overviews identified (2000–2018), 172 (32%) overlapped across similar PICO. The overlapping overviews fell within 13 WHO ICD-10 medical classifications and 63 topics. The overviews may have overlapped partially or completely, such that a similar portion, major component(s), or complete representation of an overview was duplicated. 149/172 (87%) overlapping overviews were characterized as broad in scope. Most frequently, broad overviews had targeted populations for which multiple interventions were addressed (44%), or least frequently, they had a targeted intervention for multiple populations (17%). Conclusions One third of overviews overlapped in content with a majority covering broad topic areas, and fewer considering subsets of the evidence. A multiplicity of overviews on the same topic adds to the ongoing waste of research resources, time and effort across medical disciplines. This study and the database of 172 overlapping overviews can provide a guide to authors about which topics are covered, and gaps in the evidence for future analysis.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.840 | 0.438 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.007 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.000 |
| Open science | 0.012 | 0.022 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".