Quadriceps neuromuscular function and self-reported functional ability in knee osteoarthritis
Bibliographic record
Abstract
The purposes of this study were to determine 1) the relationships of self-reported function scores in patients with knee osteoarthritis (OA) to both maximal isometric torque and to isotonic power at a variety of loads, and 2) the degree to which muscle volume (MV) or voluntary activation (VA) are associated with torque and power measures in this population. Isometric maximal voluntary contraction (MVC) torque and isotonic power [performed at loads corresponding to 10, 20, 30, 40, and 50% MVC, and a minimal load ("Zero Load")] were measured in 40 participants with knee OA. Functional ability was measured with the Western Ontario and McMaster Osteoarthritis Index (WOMAC) function subscale. MV was determined with magnetic resonance imaging, and VA was measured with the interpolated twitch technique. In general, power measured at lower loads (Zero Load and 10-30% MVC, r(2) = 0.21-0.28, P < 0.05) predicted a greater proportion of the variance in function than MVC torque (r(2) = 0.18, P < 0.05), with power measured at Zero Load showing the strongest association (r(2) = 0. 28, P < 0.05). MV was the strongest predictor of MVC torque and power measures in multiple regression models (r(2) = 0.42-0.72). VA explained only 6% of the variance in MVC torque and was not significantly associated with power at any load (P > 0.05). Quadriceps MVC torque and power are associated with self-reported function in knee OA, but muscle power at lower loads is more predictive of function than MVC torque. The variance in MVC torque and power between participants is due predominantly to differences in MV and has little to do with deficits in VA.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 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".