3D curvature of muscle fascicles in triceps surae
Bibliographic record
Abstract
Muscle fascicles curve along their length, with the curvatures occurring around regions of high intramuscular pressure, and are necessary for mechanical stability. Fascicles are typically considered to lie in fascicle planes that are the planes visualized during dissection or two-dimensional (2D) ultrasound scans. However, it has previously been predicted that fascicles must curve in three-dimensional (3D) and thus the fascicle planes may actually exist as 3D sheets. 3D fascicle curvatures have not been explored in human musculature. Furthermore, if the fascicles do not lie in 2D planes, then this has implications for architectural measures that are derived from 2D ultrasound scans. The purpose of this study was to quantify the 3D curvatures of the muscle fascicles and fascicle sheets within the triceps surae muscles and to test whether these curvatures varied among different contraction levels, muscle length, and regions within the muscle. Six male subjects were tested for three torque levels (0, 30, and 60% maximal voluntary contraction) and four ankle angles (-15, 0, 15, and 30° plantar flexion), and fascicles were imaged using 3D ultrasound techniques. The fascicle curvatures significantly increased at higher ankle torques and shorter muscle lengths. The fascicle sheet curvatures were of similar magnitude to the fascicle curvatures but did not vary between contractions. Fascicle curvatures were regionalized within each muscle with the curvature facing the deeper aponeuroses, and this indicates a greater intramuscular pressure in the deeper layers of muscles. Muscle architectural measures may be in error when using 2D images for complex geometries such as the soleus.
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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 | 0.001 |
| 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) | 0.001 | 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".