Evidence-based hierarchy of pain outcome measures for osteoarthritis clinical trials and meta-analyses
Bibliographic record
Abstract
OBJECTIVE: To rank commonly used patient-reported outcome measures (PROMs) for assessing pain in osteoarthritis trials according to their assay sensitivity, defined as the ability of a PROM to distinguish an effective from a less effective intervention or placebo, proposing a hierarchy for PROM selection in trials and data-extraction in meta-analyses. DESIGN: Analysis of trials with placebo, sham, or non-intervention control that included ≥100 patients per arm with knee/hip osteoarthritis, reporting treatment effects on ≥2 pain PROMs. Treatment effects from all PROMs were standardized on a 0-100 scale. Negative mean differences indicated a larger effect of the experimental treatment compared to control. We ranked PROMs by assay sensitivity using a Bayesian multi-outcome synthesis random-effects model. RESULTS: 135 trials comprising 57,141 participants were included. The ranking of PROMs from highest to lowest assay sensitivity was as follows: pain overall, pain on stairs, pain at night, pain on walking, pain at rest, WOMAC pain, WOMAC global, Lequesne index. Pain overall, the highest-ranked PROM, had a pooled mean difference of -6.96 (95%CrI -7.94, -6.02), while WOMAC pain, the most reported PROM in our study, had a pooled mean difference of -4.90 (95%CrI -5.55, -4.26). The pooled ratio of mean differences between pain overall and WOMAC pain was 1.42 (95%CrI 1.30, 1.55), representing a 42% larger effect size with pain overall. CONCLUSIONS: Pain overall has better assay sensitivity than other pain PROMs. Investigators should consider the hierarchy proposed in this study to guide PROM selection in osteoarthritis clinical trials and data extraction in osteoarthritis meta-analyses.
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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 | 0.080 | 0.161 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.041 | 0.056 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".