Shear wave propagation in the Achilles subtendons is modulated by helical twist and non-uniform loading
Bibliographic record
Abstract
The triceps surae is composed of the medial gastrocnemius, lateral gastrocnemius and soleus muscles. Each muscle inserts onto a subtendon that undergoes helical twist prior to insertion onto the calcaneus. Shear wave tensiometry is a non-invasive technique to gauge Achilles tendon loading, yet it is unknown whether subtendon loading can be resolved using subtendon-specific shear wave speeds. The objective of this study was to examine shear wave propagation in the lateral gastrocnemius, medial gastrocnemius and soleus subtendons of the free Achilles tendon. We expected to show that the helical arrangements of subtendons within the Achilles would modulate wave propagation, and that non-uniform loading between subtendons would elicit nonuniform wave propagation. We created a finite element model of the Achilles tendon and simulated shear wave propagation. We found that helical subtendon twist had little effect on wave propagation speed. When a two-fold stress differential was applied to the gastrocnemius and soleus subtendons, the shear wave speed-axial stress relationship was modulated by adjacent subtendon tension and the amount of overall subtendon twist. These findings enhance the basis for tensiometry in the Achilles tendon and inform causes for variability in shear wave speeds measured using shear wave tensiometry or elastography.
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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".