Fatigue Effects on the Lower Leg Muscle Architecture Using Diffusion Tensor MRI
Bibliographic record
Abstract
Proton density (PD) and diffusion tensor imaging (DTI) are imaging techniques that enable the acquisition of data from living subjects that can be used in the fine-tuning of subject-specific models’ architectural parameters. The aim of this study was to determine the in vivo 3D architectural parameters (volume, pennation angle, fiber length and physiological cross-sectional area) of the gastrocnemius medialis, gastrocnemius lateralis, soleus and tibialis anterior muscles using proton density and diffusion tensor imaging data before and after an exhaustive one-legged jump exercise. These methods were used in the in vivo 3D data acquisition of six young and physically active female subjects’ lower legs, followed by a fiber-tracking algorithm and analysis tools. No significant differences were found in the muscles’ architecture after the exercise, with the following exceptions: the anatomical cross-section area of the gastrocnemius medialis increased (p-value 0.001, effect size 0.18) after exercise; the fiber lengths of the gastrocnemius medialis, lateralis and soleus muscles were higher after exercise (p-value 0.002, 0.001 and 0.001, respectively, and effect size 2.03, 1.29 and 0.85, respectively); and the soleus mean pennation angle decreased after exercise (p-value 0.0015, effect size 2.31). These changes (or lack thereof) could be attributed to the extended acquisition time of the MRI scans to minimize noise: by increasing the acquisition time, the effect of the exercise may have been partially lost due to muscle recovery.
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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 | 0.001 | 0.001 |
| 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".