Ultrasound-derived changes in thickness of human ankle plantar flexor muscles during walking and running are not homogeneous along the muscle mid-belly region
Bibliographic record
Abstract
Abstract Skeletal muscle thickness is a valuable indicator of several aspects of a muscle’s functional capabilities. We used computational analysis of ultrasound images, recorded from 10 humans walking and running at a range of speeds (0.7–5.0 m s−1), to quantify interactions in thickness change between three ankle plantar flexor muscles (soleus, medial and lateral gastrocnemius) and quantify thickness changes at multiple muscle sites within each image. Statistical analysis of thickness change as a function of stride cycle (1d statistical parametric mapping) revealed significant differences between soleus and both gastrocnemii across the whole stride cycle as they bulged within the shared anatomical space. Within each muscle, changes in thickness differed between measurement sites but not locomotor condition. For some of the stride, thickness measures taken from the distal-mid image region represented the mean muscle thickness, which may therefore be a reliable region for these measures. Assumptions that muscle thickness is constant during a task, often made in musculoskeletal models, do not hold for the muscles and locomotor conditions studied here and researchers should not assume that a single thickness measure, from one point of the stride cycle or a static image, represents muscle thickness during dynamic movements.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".