The strength of associations between ultrasound measures of upper limb muscle morphology and isometric muscle strength: <scp>An</scp> exploratory study
Bibliographic record
Abstract
INTRODUCTION/AIMS: Assessing upper limb muscle strength is important for understanding health outcomes, such as daily function and mortality. Ultrasound (US) is increasingly used to evaluate muscle health, but the relationship between its measures of morphology and isometric strength has not been thoroughly explored in upper limb muscles. The aim of this study was to evaluate the associations between US morphological measures and isometric strength in functionally relevant upper limb muscles in healthy adults. METHODS: Twenty-four healthy volunteers (30.0 ± 10.8 years) underwent B-mode, axial US scans of the first dorsal interosseus (FDI), flexor pollicis longus (FPL), biceps brachii (BB), brachialis (BR), and triceps brachii lateral head (TB). Participants performed corresponding maximal voluntary contractions (MVC), including first digit distal phalanx flexion, second digit abduction, and elbow flexion and extension. US images were segmented to obtain maximal muscle thickness (MT) and cross-sectional area (CSA). RESULTS: Strong positive correlations were found between muscle strength and BB MT (r = .83; p < .001), BR CSA (r = .84; p < .001), and TB MT (r = .70; p < .001). Moderate positive correlations were found for strength and FDI CSA (r = .67; p < .001), FDI MT (r = .47; p < .05), FPL CSA (r = .54; p < .01), and FPL MT (r = .42; p < .05). No significant correlation was found between strength and BR MT (r = .16; p > .05). DISCUSSION: Our data showed moderate-to-strong associations between US muscle morphology and strength, suggesting that US is likely a good biomarker for strength. However, its use is not "one size fits all." Future investigations should continue to assess this relationship in different muscles and expand the generalizability to clinical populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".