Psychometric properties of the knee osteoarthritis pain index
Bibliographic record
Abstract
OBJECTIVES: The current study sought to evaluate the psychometric properties of a newly developed Knee OsteoArthritis Pain Index (KOAPI), derived from the Brief Pain Inventory (BPI), among individuals with knee osteoarthritis (KOA). METHODS: This study consisted of secondary data analysis of two clinical trials. In study 1, 241 individuals with KOA were evaluated before total knee arthroplasty and six months post-surgery. In study 2, 37 individuals with KOA participated in a randomized, double-blind, placebo controlled, two-way crossover study in which they received either a COX-2 inhibitor followed by a placebo or a placebo followed by a COX-2 inhibitor. The KOAPI was derived from the BPI and included three BPI pain severity items (worst, average, current) and the BPI pain interference item related to pain when walking. RESULTS: The KOAPI showed excellent model fit (CFI = 0.99; TFI: 0.98-0.99; RMSEA: 0.08-0.001), good reliability (Cronbach's alpha: 0.84-0.87) and high convergent validity with the Western Ontario and McMaster Universities Osteoarthritis Index (r = 0.66; 95% CI: 0.44, 0.81) and the Pain Catastrophizing Scale (r = 0.50; 95% CI: 0.39, 0.60). CONCLUSIONS: Overall, the psychometric properties of the KOAPI were comparable or better than those produced by the original BPI pain severity subscale. The KOAPI may be a helpful screening and outcome measure for individuals with KOA that more closely captures symptoms which drive patients to seek clinical care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".