Systematic reviews, overviews of reviews and comparative effectiveness reviews: a discussion of approaches to knowledge synthesis
Bibliographic record
Abstract
BACKGROUND: The Cochrane Collaboration has been at the forefront of developing methods for knowledge synthesis internationally. OBJECTIVES: We discuss three approaches to synthesize evidence for healthcare interventions: systematic reviews (SRs), overviews of reviews and comparative effectiveness reviews. METHODS: We illustrate these approaches with examples from knowledge syntheses on interventions for bronchiolitis, a common acute paediatric condition. Some of the differences among these approaches are subtle and methods are not necessarily mutually exclusive to a single review type. RESULTS AND CONCLUSIONS: Systematic reviews bring together evidence from multiple studies in a rigorous fashion for a single intervention or group of interventions. Systematic reviews, as they have developed within healthcare, often focus on single or select interventions and direct pairwise comparisons; therefore, end-users may need to read several individual SRs to inform decision making. Overviews of reviews compile information from multiple SRs relevant to a single health problem. Overviews provide the end-user with a quick overview of the available evidence; however, overviews are dependent on the methods and decisions employed at the SR level. Furthermore, overviews do not often integrate evidence from different SRs quantitatively. Comparative effectiveness reviews, as we define them here, synthesize relevant evidence from individual studies to describe the relative benefits (or harms) of a range of interventions. Comparative effectiveness reviews may use statistical methods (network meta-analysis) to incorporate direct and indirect evidence; therefore, they can provide stronger inferences about the relative effectiveness (or safety) of interventions. While potentially more expensive and time-consuming to produce, a comparative effectiveness review provides a synthesis of a range of interventions for a given condition and the relative efficacy across interventions using consistent and standardized methodology.
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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 | 0.467 | 0.675 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.023 | 0.017 |
| Bibliometrics | 0.064 | 0.069 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.017 | 0.018 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".