Critical elements of synthesis questions are incompletely reported: survey of systematic reviews of intervention effects
Bibliographic record
Abstract
OBJECTIVES: To examine the characteristics of population, intervention and outcome groups and the extent to which they were completely reported for each synthesis in a sample of systematic reviews (SRs) of interventions. STUDY DESIGN AND SETTING: We coded groups that were intended (or used) for comparisons in 100 randomly sampled SRs of public health and health systems interventions published in 2018 from the Health Evidence and Health Systems Evidence databases. RESULTS: Authors commonly used population, intervention and outcome groups to structure comparisons, but these groups were often incompletely reported. For example, of 41 SRs that identified and/or used intervention groups for comparisons, 29 (71%) identified the groups in their methods description before reporting of the results (e.g., in the Background or Methods), 12 (29%) defined the groups in enough detail to replicate decisions about which included studies were eligible for each synthesis, 6 (15%) provided a rationale, and 24 (59%) stated that the groups would be used for comparisons. Sixteen (39%) SRs used intervention groups in their synthesis without any mention in the methods. Reporting for population, outcome and methodological groups was similarly incomplete. CONCLUSION: Complete reporting of the groups used for synthesis would improve transparency and replicability of reviews, and help ensure that the synthesis is not driven by what is reported in the included studies. Although concerted effort is needed to improve reporting, this should lead to more focused and useful reviews for decision-makers.
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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 | 0.779 | 0.949 |
| Meta-epidemiology (narrow) | 0.007 | 0.009 |
| Meta-epidemiology (broad) | 0.017 | 0.021 |
| Bibliometrics | 0.030 | 0.029 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.019 | 0.019 |
| 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".