In‐vivo quantification of 3D muscle architecture in Triceps Surae muscle
Bibliographic record
Abstract
Fascicle orientation is an important architectural parameter affecting the mechanical function of skeletal muscle. Most previous studies on fascicle architecture have been in 2D and the importance of 3 rd dimension is not much explored. The 3D orientation of the whole muscle may be regionalized and is not uniform across the whole muscle. We have quantified the muscle fascicle orientation in soleus and gastrocnemii muscles in six male subjects for three contraction states (0%, 30% and 60% of MCV) and four ankle angles ( −15°, 0°, 15° and 30° of planter flexion) using B‐mode ultrasound and optical tracking systems. Images were obtained from multiple scans of the muscles with scan times less than two minutes. The images were analyzed for 3D fascicle orientation represented in a muscle‐based spherical co‐ordinate system in terms of (pennation) angle β with the long axis of the muscle and angle ϕ made by the projection of the fascicle in the transverse plane of the muscle. Both β and ϕ were regionalized across the muscles and changed with the different ankle angles and contraction states (p<0.01). Muscle fascicles are arranged in sheets and these sheets can bulge when a muscle contracts and hence the fascicle may no longer lie in a 2D plane. We suggest that the fascicle arrangement changes in response to intramuscular pressure, and these changes alter the mechanical output. This project is funded by NSERC.
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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 | 0.000 |
| 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) | 0.001 | 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".