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 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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".