Falls Associated with Muscle Strength in Patients with Knee Osteoarthritis and Self-reported Knee Instability
Bibliographic record
Abstract
OBJECTIVE: We aimed to evaluate the associations between knee muscle strength (MS) and falls, controlling for knee joint proprioception, varus-valgus knee joint laxity, and knee pain, among patients with knee osteoarthritis (OA) reporting knee instability. METHODS: A sample of 301 subjects (203 women, 98 men, 35-82 yrs) with established knee OA and self-reported knee instability was studied. The occurrence of at least 1 fall in the previous 3 months was assessed by questionnaire. Maximum knee extension and flexion strength were measured isokinetically. Additionally, proprioception, varus-valgus laxity, and pain were assessed. Student t tests were used to assess differences between subgroups. The association of muscle strength and falls was calculated using univariate and multivariate logistic regression analysis. RESULTS: Over 10% of the subjects (31 out of 301) reported a fall in the previous 3 months. High knee extension muscle strength (crude OR 0.3, 95% CI 0.1-0.8, p = 0.022) and high knee flexion muscle strength (crude OR 0.2, 95% CI 0.0-1.0, p = 0.048) were associated with a lower risk of falls. Proprioception and laxity did not confound this relationship. After adjusting for pain, extensor strength had an adjusted OR of 0.5 (95% CI 0.2-1.4, p = 0.212) for falls and flexor strength had an adjusted OR of 0.4 (95% CI 0.1-2.3, p = 0.312). CONCLUSION: High knee extension and flexion muscle strength decreased the risk of falls in patients with knee OA and self-reported knee instability. After considering the effect of pain, there was insufficient statistical power to detect an association between muscle strength and falls, which might be because of the low number of subjects who fell (n = 31).
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".