Skeletal muscle mass and distribution in 468 men and women aged 18–88 yr
Bibliographic record
Abstract
We employed a whole body magnetic resonance imaging protocol to examine the influence of age, gender, body weight, and height on skeletal muscle (SM) mass and distribution in a large and heterogeneous sample of 468 men and women. Men had significantly (P < 0.001) more SM in comparison to women in both absolute terms (33.0 vs. 21.0 kg) and relative to body mass (38.4 vs. 30.6%). The gender differences were greater in the upper (40%) than lower (33%) body (P < 0.01). We observed a reduction in relative SM mass starting in the third decade; however, a noticeable decrease in absolute SM mass was not observed until the end of the fifth decade. This decrease was primarily attributed to a decrease in lower body SM. Weight and height explained approximately 50% of the variance in SM mass in men and women. Although a linear relationship existed between SM and height, the relationship between SM and body weight was curvilinear because the contribution of SM to weight gain decreased with increasing body weight. These findings indicate that men have more SM than women and that these gender differences are greater in the upper body. Independent of gender, aging is associated with a decrease in SM mass that is explained, in large measure, by a decrease in lower body SM occurring after the fifth decade.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
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".