Responsiveness of Single versus Composite Measures of Pain in Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: In rheumatoid arthritis, composite outcomes constructed from a combination of outcome measures are widely used to enhance responsiveness (sensitivity to change) and comprehensively summarize response. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain is the primary outcome measure in many osteoarthritis (OA) trials. Information from other outcomes, such as rescue medication use and other WOMAC subscales, could be added to create composite outcomes, but the sensitivity of such a composite has not been tested. METHODS: We used data from a completed trial of tanezumab for knee OA (NCT00733902). The WOMAC questionnaire and rescue medication use were measured at several timepoints, up to 16 weeks. Pain and rescue medication outcomes were standardized and combined into 3 composite outcomes through principal components analysis to produce 1 score (composite outcome) and their responsiveness was compared to WOMAC pain, the standard. We pooled all treatment doses of tanezumab into 1 treatment group, for simplicity, and compared this to the control group (placebo). RESULTS: The composite outcomes showed modestly, but not statistically significantly greater responsiveness when compared to WOMAC pain alone. Adding information on rescue medication to the composite improved responsiveness. While improvements in sensitivity were modest, the required sample sizes for trials using composites was 20-40% less than trials using WOMAC pain alone. CONCLUSION: Combining information from related but distinct outcomes considered relevant to a particular treatment improved responsiveness, could reduce sample size requirements in OA trials, and might offer a way to better detect treatment efficacy in OA trials.
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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 | 0.153 | 0.229 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".