Contribution of Quadriceps Weakness to Fragility Fracture: A Prospective Study
Bibliographic record
Abstract
The association between muscle weakness and fracture is not well understood. This study sought to examine the contribution of muscle strength at baseline and change in muscle strength to the observed risk of fragility fracture in older people. The study involved 595 men and 1066 women aged 60+ years (median 69 years) who had been followed for a median of 11 years (range, 4 to 22 years). Quadriceps isometric muscle strength (MS) measured at baseline and biennially was adjusted for height. Femoral neck bone mineral density (FNBMD) was measured by DXA. Low-trauma fracture was ascertained from X-ray reports and interview. The relationship between baseline MS and serial MS and fracture assessed by time-invariant and time-variant Cox's regression models was expressed as hazard ratio (HR) and 95% confidence interval (CI). During the follow-up period, 282 (26%) women and 89 (15%) men sustained a fragility fracture. From age 60 years, women lost 0.28 kg/m (1.6%) of MS per year, whereas men lost 0.39 kg/m (1.5%) of MS per year. In the time-variant model, using serial MS, each 1 SD (4.7 kg/m) lower MS was associated with a 27% increase in the risk of fracture in women (HR 1.27; 95% CI, 1.11 to 1.43); and 46% increase in men (HR 1.46; 95% CI, 1.22 to 1.75). After adjusting for FNBMD, age and prior fracture, history of fall and smoking, HR per SD of lower MS was 1.13 (95% CI, 0.99 to 1.28) for women and 1.35 (95% CI, 1.18 to 1.64) for men. These data indicate that muscle weakness is an independent determinant of fracture risk in men, but not in women. This sex difference suggests that apart from mechanical load effect of muscle on bone, there are other muscle-bone interactions that need to be investigated in future studies. The accuracy of fracture risk prediction for men may be improved by incorporating muscle strength.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".