The construct validity and responsiveness of measures of the predictability of intermittent knee pain in knee osteoarthritis
Bibliographic record
Abstract
Objective: The Intermittent and Constant Osteoarthritis Pain (ICOAP) questionnaire was developed to assess the osteoarthritis (OA) pain experience, but does not currently incorporate pain predictability, which people with OA considered important. We assessed the construct validity and responsiveness of two supplemental questions assessing the frequency of predictable and unpredictable intermittent knee pain. Design: This was a secondary analysis of a prospective cohort of individuals aged 30 years or older undergoing total knee arthroplasty, TKA, for knee OA. Standardized questionnaires assessed socio-demographics, health status, ICOAP intermittent and constant pain subscales, the frequency of predictable and unpredictable intermittent knee pain (5-point Likert scale from never to very often), KOOS-QOL, and two ICOAP comparator measures (Pain Catastrophizing, and Perceived Arthritis Coping Efficacy), pre- and 12-months post-TKA. Construct validity was assessed by examining the Spearman correlations between pre-TKA predictability scores, ICOAP, KOOS QOL and comparator measures. Responsiveness was assessed with standardized response means (SRMs) for pre-post TKA change in predictability scores. Results: Of 1366 participants (mean age 67.2 years; 60.8 % female), 1302 had intermittent knee pain. Predictable and unpredictable pain were reported 'very often' by 35.3 % and 18.1 %, respectively. Spearman correlations for frequency of predictable intermittent knee pain with KOOS QOL, PCS and Coping Efficacy were 0.39, 0.32 and -0.15, respectively; for unpredictable pain, correlations were 0.46, 0.46 and -0.27, respectively (p < 0.0001 for all). SRMs were 1.23 for predictable and 1.31 for unpredictable pain. Conclusion: Our findings support the construct validity and responsiveness of the two predictability items.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".