Presentation approaches for enhancing interpretability of patient-reported outcomes in meta-analyses: a systematic survey of Cochrane reviews
Bibliographic record
Abstract
OBJECTIVES: To systematically survey Cochrane reviews' approaches to calculating, presenting, and interpreting pooled estimates of patient-reported outcome measures (PROMs). STUDY DESIGN AND SETTING: We retrospectively selected 200 Cochrane reviews that met the eligibility criteria. Two researchers independently extracted the pooled effect measures and approaches for pooling and interpreting the effect measures, reaching consensus through discussions. RESULTS: When primary studies used the same PROM, Cochrane review authors most often used mean differences (MDs) (81.9%) for calculating the pooled effect measures; when primary studies used different PROMs, the review authors often applied standardized mean differences (SMDs) (54.3%). Although in most cases (80.1%) the review authors interpreted the importance of effect, they failed, in 48.5% of the pooled effect measures, to report criteria for categorizing the magnitude of effect. When authors interpreted the importance of the effect, for those with primary studies using the same PROM, they most often referred to the minimally important differences (MIDs) (75.0%); for those with primary studies using different PROMs, the approaches used varied. CONCLUSION: Cochrane review authors most often used MDs or SMDs for calculating and presenting the pooled effect measures of PROs but often failed to make explicit their criteria for categorizing the magnitude of effect.
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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 | 0.638 | 0.863 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.082 | 0.052 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".