Magnetic Resonance Imaging–Assessed Vastus Medialis Muscle Fat Content and Risk for Knee Osteoarthritis Progression: Relevance From a Clinical Trial
Bibliographic record
Abstract
OBJECTIVE: Studies have proposed vastus medialis (VM) muscle cross-sectional area change as a variable associated with cartilage volume loss in knee osteoarthritis (OA). However, the VM also includes fat (%Fat), which may influence knee function. This study analyzed the VM area and %Fat data, separately and in combination, to predict symptoms, cartilage volume loss, and bone marrow lesion (BML) change in knee OA. METHODS: This study included the according-to-protocol population (n = 143) of a 2-year knee OA randomized clinical trial having magnetic resonance imaging at baseline and 2 years. Correlations used multivariate analyses. RESULTS: Greater baseline value for VM area and %Fat were significantly associated with sex (male, area; female, %Fat), higher body mass index (BMI), and Western Ontario and McMaster Universities Osteoarthritis Index stiffness, function, and total scores (better, high area; worse, high %Fat). Moreover, a VM %Fat increase of 1% at 2 years was associated with worsening of cartilage volume loss in the global knee (P = 0.015) and some subregions (P ≤ 0.030), and with an increment of BML global score change (P < 0.001). A 1% decrease in VM area at 2 years was associated with worsening of knee pain score (P = 0.048). Importantly, the concurrent presence of low VM area, high VM %Fat, and high BMI identified a subgroup of patients with greater cartilage volume loss in the medial femur (P = 0.028) than the rest of the cohort. CONCLUSION: These data demonstrated, for the first time, that VM fat content is a strong predictor of cartilage volume loss and the occurrence and progression of BML. Importantly, the combined data of VM area, VM %Fat, and BMI identified patients at higher risk for OA progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".