Stretch training induces unequal adaptation in muscle fascicles and thickness in medial and lateral gastrocnemii
Bibliographic record
Abstract
This study compared adaptations in fascicle lengths, pennation angles, and muscle thickness of the lateral and medial gastrocnemii in response to 6 weeks of stretch training. The nondominant plantar flexors of 11 males were stretched five times per week for 6 weeks and compared with the contralateral leg and a nonstretched control group of 10 males. During stretch training, instantaneous electromyography was utilized to ensure passive muscle stretch. At baseline, week three, week six and 1 week after the conclusion of stretch training, ultrasound was used to measure fascicle lengths, pennation angles, muscle thickness of the lateral gastrocnemius and medial gastrocnemius, and Achilles tendon thickness and length. Plantar flexion torque was measured, and voluntary activation was assessed. Muscle thickness increased 5.6% after 6 weeks of stretch training (P=.009). The fascicles in the lateral gastrocnemius lengthened to a greater extent than the medial. Overall, fascicles lengthened 25% (P<.001) in the muscle tendon junction and 5.1% (P<.001) in the muscle belly. Pennation angles were unchanged in the medial gastrocnemius but decreased in the lateral gastrocnemius 7.1% (P=.02). There was no change in maximal voluntary contraction, voluntary activation, tendon length, or thickness. This study demonstrates that stretch training is a viable modality to alter muscle architecture of the human gastrocnemius through lengthening of muscle fascicles, decreasing pennation angles, and increasing muscle thickness, albeit adaptations are unequal between the lateral and medial heads.
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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".