Patient-reported outcomes in meta-analyses – Part 1: assessing risk of bias and combining outcomes
Bibliographic record
Abstract
Systematic reviews and meta-analyses of randomized trials that include patient-reported outcomes (PROs) often provide crucial information for patients and clinicians facing challenging health care decisions. Based on emerging methods, guidance on combining PROs in meta-analysis is likely to enhance their usefulness.The objectives of this paper are: i) to describe PROs and why they are important for health care decision-making, ii) illustrate the key risk of bias issues that systematic reviewers should consider and, iii) address outcome characteristics of PROs and provide guidance for combining outcomes.We suggest a step-by-step approach to addressing issues of PROs in meta-analyses. Systematic reviewers should begin by asking themselves if trials have addressed all the important effects of treatment on patients' quality of life. If the trials have addressed PROs, have investigators chosen the appropriate instruments? In particular, does evidence suggest the PROs used are valid and responsive, and is the review free of outcome reporting bias? Systematic reviewers must then decide how to categorize PROs and when to pool results.
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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 | 0.518 | 0.713 |
| Meta-epidemiology (narrow) | 0.010 | 0.006 |
| Meta-epidemiology (broad) | 0.032 | 0.058 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".