Diffusion tensor MRI to assess skeletal muscle disruption following eccentric exercise
Bibliographic record
Abstract
INTRODUCTION: Structural evidence of exercise-induced muscle disruption has traditionally involved histological analysis of muscle tissue obtained by needle biopsy, however, there are multiple limitations with this technique. Recently, diffusion tensor magnetic resonance imaging (DT-MRI) has been successfully demonstrated to noninvasively assess skeletal muscle abnormalities induced by traumatic injury. METHODS: To determine the potential for DT-MRI to detect musculoskeletal changes after a bout of eccentric exercise, 10 healthy men performed 300 eccentric actions on an isokinetic dynamometer. DT-MRI measurements and muscle biopsies from the vastus lateralis were obtained before and 24 h post-exercise. RESULTS: Z-band streaming was higher 24 h post-exercise compared with baseline (P < 0.05). The histological indices of damage coincided with changes in DT-MRI parameters of fractional anisotropy (FA) and apparent diffusion coefficient; reflecting altered skeletal muscle geometry (P < 0.05). Z-band streaming quantified per fiber correlated with FA (r = -0.512; P < 0.05). CONCLUSIONS: DT-MRI can detect changes in human skeletal muscle structure following eccentric exercise.
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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 | 0.001 | 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".