Site‐specific skeletal muscle echo intensity and thickness differences in subcutaneous adipose tissue matched older and younger adults
Bibliographic record
Abstract
BACKGROUND: Age-related deterioration of muscle mass does not occur uniformly across the body. However, there is limited knowledge on the uniformity of age-related muscle composition changes across the body. OBJECTIVE: Our primary objective was to evaluate muscle composition differences between younger and older adults across multiple muscle groups. METHODS: We re-analysed data from a previously published cohort to evaluate differences in ultrasound muscle composition (echo intensity) between younger (<45 years) and older (>60 years) adults, when matched for adipose tissue mass at the anterior upper arm, anterior upper leg and abdominal muscles. Analysis of echo intensity is confounded by subcutaneous adipose tissue (SAT) thickness overlaying the muscle; we accounted for these effects by matching older and younger adults (1:1), stratified by sex, for absolute SAT thickness at each landmark. RESULTS: From 96 adults (n = females), 58 (n = 34) were SAT matched at the anterior upper arm, 52 (n = 30) at the anterior upper leg and 60 (n = 30) at the abdominal region; thus, there were no age group differences in SAT thickness at each landmark. In comparison with younger adults, older adults presented with greater echo intensity at the anterior upper leg (females:40.3 ± 6.8 vs. 52.4 ± 7.6; males:35.7 ± 8.0 vs. 54.3 ± 9.8, p < .01) and abdominal (females:38.7 ± 27.6 vs. 73.4 ± 31.0; males:18.7 ± 15.2 vs. 60.9 ± 23.4, p < .01) muscles, but not anterior upper arm muscles (females:47.0 ± 6.5 vs. 53.2 ± 13.1; males:43.4 ± 8.9 vs. 48.9 ± 10.1, p = .18). CONCLUSIONS: Distinct age-related differences in trunk and lower limb muscle composition were evident compared to upper limb muscles; highlighting the importance of quantifying specific muscle groups when evaluating age-associated muscle characteristics.
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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 | 0.001 |
| 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) | 0.002 | 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".