Muscle Power Is an Independent Determinant of Pain and Quality of Life in Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: This study examined the relationships between leg muscle strength, power, and perceived disease severity in subjects with knee osteoarthritis (OA) in order to determine whether dynamic leg extensor muscle power would be associated with pain and quality of life in knee OA. METHODS: Baseline data on 190 subjects with knee OA (mean ± SD age 60.2 ± 10.4 years, body mass index 32.7 ± 7.2 kg/m(2) ) were obtained from a randomized controlled trial. Knee pain was measured using the Western Ontario and McMaster Universities Osteoarthritis Index, and health-related quality of life was assessed using the Short Form 36 (SF-36). One-repetition maximum (1RM) strength was assessed using the bilateral leg press, and peak muscle power was measured during 5 maximum voluntary velocity repetitions at 40% and 70% of 1RM. RESULTS: In univariate analysis, greater muscle power was significantly associated with pain (r = -0.17, P < 0.02) and also significantly and positively associated with SF-36 physical component summary (PCS) scores (r = 0.16, P < 0.05). After adjustment for multiple covariates, muscle power was a significant independent predictor of pain (P ≤ 0.05) and PCS scores (P ≤ 0.04). However, muscle strength was not an independent determinant of pain or quality of life (P ≥ 0.06). CONCLUSION: Muscle power is an independent determinant of pain and quality of life in knee OA. Compared to strength, muscle power may be a more clinically important measure of muscle function within this population. New trials to systematically examine the impact of muscle power training interventions on disease severity in knee OA are particularly warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".