Effect of Knee Extensor Power on Knee Pain in Adults With or at Risk for Osteoarthritis: The Multicenter Osteoarthritis Study
Bibliographic record
Abstract
OBJECTIVE: Knee extensor power declines rapidly with aging and may contribute to knee pain. We evaluated the relationship between knee extensor power and changes in knee pain over 2 years in adults with or at risk for knee osteoarthritis (OA). METHODS: We used data from the Multicenter Osteoarthritis Study (MOST). Knee extensor power was measured at baseline using isotonic contractions at 40% of 1-repetition maximum. Pain severity (Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC]) and frequent knee pain (FKP; pain on most days in the past 30 days) were assessed at baseline, 8, 16, and 24 months in each knee. We examined the association between baseline sex-specific quartiles of knee extensor power and 2 outcomes-worsening WOMAC pain and incident FKP-using logistic regression with generalized estimating equations to account for within-subject correlations between knees. RESULTS: for linear trend < 0.01) the odds of incident FKP. CONCLUSION: Lower knee extensor power may be a risk factor for both worsening knee pain severity and the development of FKP in adults with or at risk for knee OA. Interventions targeting knee extensor power may reduce the risk for incident and progressive knee pain.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".