Ultrasound image resolution influences analysis of skeletal muscle composition
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Analysis of muscle composition using ultrasound requires standardization of several equipment settings (i.e. gain). However, the influence of image resolution, which is altered by imaging depth, on measures of muscle composition is unknown. METHODS: We analysed rectus femoris muscle composition using ultrasound images captured from 32 males and females (aged 28 ± 5 years) at depths of 9.0, 7.3, 5.9 and 4.7 cm. The transducer's orientation was fixed using a clamp during image acquisition to minimize movement. Across each image resolution, a region of interest encompassing the same anatomical area within the muscle was used for muscle composition analysis. Muscle composition was analysed using a combination of first-, second- and higher-order texture features. Muscle composition agreement across image resolutions was evaluated using a one-way ANOVA and intraclass correlation coefficients (ICC). RESULTS: Most muscle composition features displayed differences due to image resolution (p < .05). ICCs demonstrated poor-to-good agreement across different image resolutions. In general, higher resolution images (i.e. shallower imaging depth) demonstrated better agreement (ICC > 0.90) compared to lower resolution images. CONCLUSIONS: Ultrasound image resolution influences muscle composition analysis. Image resolution should be fixed within and between individuals when evaluating muscle composition using ultrasound.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it