Muscle function, quality, and relative mass are associated with knee pain trajectory over 10.7 years
Bibliographic record
Abstract
ABSTRACT: Periarticular muscle plays an important role in the pathogenesis of musculoskeletal pain. We recently reported that pain population consists of distinct subgroups of which the causes and mechanisms may differ. This study aimed to examine the association of lean mass, muscle strength, and quality with 10.7-year pain trajectory. Nine hundred forty-seven participants from a population-based cohort study were analysed. Dual-energy X-ray absorptiometry was used to assess lean and fat mass. Leg strength, knee extensor strength, and lower-limb muscle quality were measured/calculated. Knee pain was assessed by the Western Ontario and McMaster Universities Osteoarthritis Index pain questionnaire. Radiographic knee osteoarthritis was assessed by X-ray. Three distinct pain trajectories were identified: "Minimal pain" (53%), "Mild pain" (34%), and "Moderate pain" (13%). Higher total and lower-limb lean mass were associated with an increased risk of "Mild pain" and "Moderate pain" trajectories relative to the "Minimal pain" trajectory group, but these associations became nonsignificant after further adjustment for fat mass. Total lean mass percentage was associated with a lower risk of "Mild pain" (relative risk ratio [RRR]: 0.95, 95% confidence interval 0.92-0.98) and "Moderate pain" trajectory (RRR: 0.92, 95% confidence interval 0.87-0.96). Greater leg and knee extensor strength and muscle quality were associated with "Mild pain" and "Moderate pain" trajectories (RRR: 0.52-0.65, all P < 0.05). Similar results were found in those with radiographic knee osteoarthritis. Higher lower-limb muscle strength and quality, and relative lean mass, are associated with a reduced risk of severe knee pain trajectories, suggesting that improving muscle function and composition may protect against persistent unfavourable knee pain courses.
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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 | 0.001 | 0.001 |
| 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 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".