Methodological considerations for and validation of the ultrasonographic determination of human skeletal muscle hypertrophy and atrophy
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is the current gold standard for measuring changes in muscle size (cross-sectional area [CSA] and volume) but can be cost-prohibitive and resource-intensive. We evaluated the validity of B-mode ultrasonography (US) as a low-cost alternative to MRI for measuring muscle hypertrophy and atrophy in response to resistance training and immobilization, respectively. Fourteen young men performed 10wk of unilateral resistance training (RT) to induce muscle hypertrophy. In the final two weeks of the 10wk, the subjects’ contralateral leg was immobilized (IMB). The cross-sectional area of the vastus lateralis (VLCSA) was measured at the mid-thigh before and after each intervention using MRI (VLCSAMRI) and US (VLCSAUS). The relationship and agreement between methods were assessed. Reliability of US measurements ranged from good to excellent in all comparisons (ICC >0.67). VLCSA significantly increased after 10 weeks of RT (VLCSAUS: 7.9 ± 3.8%; VLCSAMRI: 7.8 ± 4.5%) and decreased after 2 weeks of IMB (VLCSAUS: −8.2%±5.8%; VLCSAMRI: −8.7 ± 6.1%). Significant correlations were identified between MRI and US at each time point measured (all r > 0.85) and, importantly, between MRI- and US-derived changes in VLCSA. Bland-Altman analysis revealed minimal bias in US measurements relative to the MRI (−0.5 ± 3.0%) and all measurements were within the upper and lower limits of agreement. Our data suggest that B-mode ultrasonography can be a suitable alternative to MRI for measuring changes in muscle size in response to increased and decreased muscle loading in young men.
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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 | 0.325 | 0.368 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".