Sensitivity to Change of Patient‐Preference Measures for Pain in Patients With Knee Osteoarthritis: Data From Two Trials
Bibliographic record
Abstract
OBJECTIVE: In osteoarthritis (OA) clinical trials, a pain measure that is most sensitive to change is considered optimal. We compared sensitivity to change of patient-reported pain outcomes, including a patient-preference measure (where the patient nominates an activity that aggravates their pain). METHODS: We used data from 2 trials of patients with confirmed (American College of Rheumatology criteria) knee OA: a trial of brace treatment for patellofemoral OA, and a trial of intraarticular steroids in knee OA. Both trials reported an improvement in pain following treatment. Participants rated pain on a 100-mm visual analog scale (VAS), in the activity that caused them the most knee pain (VASNA ), as well as completing questions on overall knee pain and the Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were also calculated from the KOOS. Standardized changes in each outcome were generated between treatment and control after 6 weeks intervention in the BRACE trial, and 1-2 weeks following intervention in the steroid trial. RESULTS: The VASNA produced standardized changes following treatment that were at least as large as other pain outcomes. In the BRACE trial, the between-groups standardized change with the VASNA was -0.63, compared with the KOOS pain subscale change of -0.33, and pain in the last week VAS change of -0.56. In the steroid study, within-group change following treatment in the VASNA was -0.60, compared to the last week VAS change of -0.51, and KOOS pain subscale change of -0.58. CONCLUSION: Pain on nominated activity appears to be at least as, and in some cases more, sensitive to change than the KOOS/WOMAC questionnaire.
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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 | 0.124 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.016 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".