Synthesis methods other than meta-analysis were commonly used but seldom specified: survey of systematic reviews
Bibliographic record
Abstract
OBJECTIVES: To examine the specification and use of summary and statistical synthesis methods, focusing on synthesis methods other than meta-analysis. STUDY DESIGN AND SETTING: We coded the specification and use of summary and synthesis methods in 100 randomly sampled systematic reviews (SRs) of public health and health systems interventions published in 2018 from the Health Evidence and Health Systems Evidence databases. RESULTS: Sixty of the 100 SRs used other synthesis methods for some (27/100) or all syntheses (33/100). Of these, 54/60 used vote counting: three based on direction of effect, 36 on statistical significance, and 15 were unclear. Eight SRs summarized effect estimates (for example, using medians). Seventeen SRs used the term 'narrative synthesis' (or equivalent) without describing methods; in practice 15 of these used vote counting. 58/100 SRs used meta-analysis. In SRs providing a rationale for not proceeding with meta-analysis, the most common reason was due to diversity in study characteristics (33/39). CONCLUSION: Statistical synthesis methods other than meta-analysis are commonly used, but few SRs describe the methods. Improved description of methods is required to allow users to appropriately interpret findings, critique methods used and verify the results. Greater awareness of the serious limitations of vote counting based on statistical significance is required.
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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 | 0.474 | 0.807 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.027 | 0.038 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".