Overview of evidence synthesis types and modes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Evidence syntheses systematically compile and analyze information from multiple sources to support health-care decision-making. As many different types of questions need to be answered in health care, different evidence synthesis types have emerged. In this article, we introduce the most common types of evidence synthesis. STUDY DESIGN AND SETTING: We discuss the aims, key methodological features, and illustrative examples of different evidence synthesis types and modes, drawing on our work with the Evidence Synthesis Taxonomy Initiative (ESTI). RESULTS: Evidence synthesis types include systematic reviews, qualitative evidence syntheses, mixed methods reviews, overviews of reviews, and 'big picture reviews' (scoping reviews, mapping reviews, and evidence gap maps). Additionally, we focus on rapid and living reviews as modes and how they can be applied to different evidence synthesis types. CONCLUSION: It is essential to understand the main types of evidence synthesis to choose the most suitable method for addressing a specific health-related question. PLAIN LANGUAGE SUMMARY: Health-care decisions should be based on the best available evidence. To bring together findings from many studies, researchers use evidence synthesis-structured methods that summarize what is known on a topic. Because health questions differ, various types of evidence syntheses exist, each designed for specific needs. This article explains the aims and characteristics of the most common types of evidence synthesis: systematic reviews, overviews of reviews, qualitative evidence syntheses, mixed methods reviews, and 'big picture reviews' (scoping reviews, mapping reviews, and evidence gap maps). We also describe two ways evidence syntheses can be carried out: rapid reviews (done quickly to support urgent decisions) and living reviews (regularly updated as new evidence becomes available). Understanding the different approaches helps clinicians, patients, and policymakers select the right type of review for their health questions. This ensures that decisions are guided by evidence that is both reliable and appropriate for the situation.
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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 | 0.299 | 0.571 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.069 | 0.044 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.030 | 0.022 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.065 | 0.018 |
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".