Community-dwelling female fallers have lower muscle density in their lower legs than non-fallers: Evidence from the Saskatoon Canadian Multicentre Osteoporosis Study (CaMos) cohort
Bibliographic record
Abstract
OBJECTIVES: Our objectives were to determine whether peripheral quantitative computed tomography (pQCT)-derived lower leg muscle density and area, and basic functional mobility differ between community-dwelling older women who do and do not report recent falls. DESIGN: Matched case-control comparison. SETTING: Academic biomedical imaging laboratory. PARTICIPANTS: 147 Women, 60 years or older (mean age 74.3 y, SD 7.7) recruited from a longitudinal, population-based cohort representing community-dwelling residents in the area of Saskatoon, Canada. MEASUREMENTS: A cross-sectional pQCT scan of the non-dominant lower leg was acquired to determine muscle density and area. Basic functional mobility (Timed Up and Go Test [TUG]) and SF36 health status were also measured. Fallers (one or more falls) and non-fallers (no falls) were grouped according to a 12-month retrospective survey and matched on measured covariates. RESULTS: The muscle density of fallers (n = 35) was a median of 2.1 mg/cm3 lower (P = 0.019, 95% C.I. -3.9 to -0.1) than non-fallers (n = 78) after matching and adjusting for age, body mass index, and SF36 general health scores. Muscle area and TUG did not differ between fallers and non-fallers. CONCLUSIONS: Muscle density may serve as a physiological marker in the assessment of lower leg muscular health and fall risk in community-dwelling elderly women. These results are limited to our study population who were mostly Caucasian. Prospective studies are required for verification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".