Predictive Capacity of Thigh Muscle Strength in Symptomatic and/or Radiographic Knee Osteoarthritis Progression
Bibliographic record
Abstract
Thigh muscle weakness is a risk factor for incident radiographic and symptomatic knee osteoarthritis (KOA). The role of thigh muscle weakness in radiographic and/or symptomatic KOA progression remains elusive. Five hundred twenty-seven knees of 527 Osteoarthritis Initiative participants with baseline Kellgren-Lawrence grades 1 to 3 were included in this nested case-control study evaluating whether baseline muscle strength predicted symptomatic and/or radiographic KOA progression. Case knees (n = 173) displayed both medial tibiofemoral joint space loss (≥0.7 mm) and a persistent increase in Western Ontario McMasters Osteoarthritis Index pain (≥9 on a 0- to 100-point scale) over 24 to 48 months from baseline. Control knees (n = 354) included 174 with neither radiographic nor symptomatic progression, 91 with radiographic progression only, and 89 with symptomatic progression only. Isometric knee extensor and flexor strength were recorded at baseline. Using logistic regression models, muscle strength was not associated with case status. However, knee extensor (odds ratio, 1.7; 95% confidence interval, 1.1-3.3; P = 0.035) and flexor weakness (odds ratio, 2.0; 95% confidence interval, 1.1, 3.3; P = 0.016) predicted isolated symptomatic progression in males, but not in females. The results indicate that thigh muscle strength may affect symptomatic and structural progression differently in males with KOA and identify an important window for potentially lowering risk of symptomatic osteoarthritis progression in men.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".