Age-, sex-, and region-specific differences in skeletal muscle size and quality
Bibliographic record
Abstract
Ultrasonography-derived cross-sectional area (CSA) and echo intensity (EI) are increasingly utilized by investigators to study muscle size and quality, respectively. We sought to examine age, sex, and region (proximal, middle, distal) differences in vastus lateralis and rectus femoris CSA and EI, and determine whether correction for subcutaneous fat thickness influences the magnitude of EI differences. Fifteen younger men (mean age = 23 years), 15 younger women (aged 21 years), 11 older men (aged 74 years), and 15 older women (aged 70 years) participated. Clear differences were observed among age, sex, and region for vastus lateralis CSA (p ≤ 0.013, d = 0.38–0.73), whereas rectus femoris CSA was only different between younger and older participants at the proximal region (p = 0.017, d = 0.65). Uncorrected EI was greatest at the distal region of both muscles (p < 0.001, d = 0.59–1.38), with only the younger men having significantly lower EI values than the other groups (p ≤ 0.043, d = 0.37–0.63). Subcutaneous fat correction resulted in a marked increase in the magnitude of sex-specific EI differences (p ≤ 0.032, d ≥ 0.42). Additionally, subcutaneous fat correction increased the uniformity of EI throughout the thigh. These findings highlight considerable region-specific differences in muscle size and quality among younger and older men and women and highlight the need to correct for subcutaneous fat thickness when examining EI. Novelty Rectus femoris CSA is similar between younger and older adults except at the most proximal site evaluated. Age- and sex-specific differences in uncorrected EI are nonuniform across the thigh. Correction for subcutaneous fat thickness substantially increased EI in women, resulting in greater sex differences.
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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) | 0.000 | 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".