Toward a sex‐specific relationship between muscle strength and appendicular lean body mass index?
Bibliographic record
Abstract
BACKGROUND: In spite of some dissociation between muscle mass and strength, muscle strength is often used as a proxy to identify individuals with low muscle mass (sarcopenia). Thus, the aim of the present study was to investigate the relationship between muscle strength and the appendicular lean body mass index (app LBMI). METHODS: One hundred and five individuals were recruited. Knee extension and handgrip strength were measured. Body composition was assessed by DXA. App LBMI was calculated as appendicular lean body mass divided by height squared. RESULTS: At le level of the entire cohort, both handgrip (r = 0.73; p < 0.001) and knee extension strength (r = 0.57; p < 0.001) were associated with app LBMI. However, in women, knee extension strength (r = 0.32; p < 0.05) but not handgrip strength (r = 0.14; p = 0.35) was associated with app LBMI; while in men, handgrip strength (r = 0.43; p < 0.01) but not knee extension strength (r = 0.27; p = 0.09) was associated with app LBMI. CONCLUSIONS: Muscle strength appears to be associated with lean body mass; however, handgrip strength may be preferentially used in men and knee extension strength in women to detect sarcopenic individuals. Future larger studies are now needed to confirm our findings and their clinical relevance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".