Relationship of intermuscular fat volume in the thigh with knee extensor strength and physical performance in women at risk of or with knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the extent to which thigh intermuscular fat (IMF) and quadriceps muscle (QM) volumes explained variance in knee extensor strength and physical performance in women with radiographic knee osteoarthritis (ROA) and without. METHODS: Baseline data from 125 women (age ≥50 years) in the Osteoarthritis Initiative study, with or at risk of knee ROA were included. Knee extensor strength was measured using a fixed force transducer, normalized to body mass (N/kg). Physical performance was the time required for 5 repeated chair stands (seconds). The IMF and QM volumes, normalized to height (cm(3) /meter), were yielded from analyses of T1-weighted axial magnetic resonance images of the midthigh. Mean IMF and QM volumes, extensor strength, and physical performance were compared between women with and without ROA, controlling for age. Hierarchical multiple regressions determined whether IMF and QM volumes were related to strength and performance after controlling for age, ROA status (yes/no), alignment, and pain. RESULTS: Compared to subjects with ROA, the subjects without ROA were stronger and performed chair stands faster (P < 0.05). After adjusting for age, those subjects without ROA had less mean ± SD IMF (61.1 ± 20.3 cm(3) /meter) compared to mean ± SD ROA (72.0 ± 25.0 cm(3) /meter; P < 0.05). In the entire sample, 21.1% of variance in knee extensor strength was explained by alignment, pain, and IMF. A model explaining 13.4% of variance in physical performance included OA status and IMF. QM volume was unrelated to strength and physical performance. CONCLUSION: IMF volume explained a small amount of variance in knee extensor strength and physical performance among women with or at risk of knee ROA.
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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 | 0.002 |
| 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".