Hamstrings passive and active shear modulus: Implications of conventional static stretching and warmup
Bibliographic record
Abstract
PURPOSE: This study compares the acute effects of a static stretching and a warmup protocol on the active and passive shear modulus of the hamstring muscles. METHODS: Muscle shear modulus was assessed at rest and during isometric contractions at 20 % of maximal voluntary isometric contraction (MVIC). RESULTS: After stretching, the passive shear modulus pattern was not altered, while at 20 % MVIC the biceps femoris short head (BFsh) and semimembranosus showed a shear modulus increase and decrease, respectively, which resulted on BFsh-SM pair differences (pre: 3.8 ± 16.8 vs. post: 39.3 ± 25.1 kPa; p < 0.001; d = 1.66) which was accompanied by a decrease of 18.3 % on MVIC. Following the warmup protocol, passive shear modulus remained unchanged, while active shear modulus was decreased for the semitendinosus (pre: 65.3 ± 13.5 vs. post: 60.3 ± 12.3 kPa; p = 0.035; d = 0.4). However, this difference was within the standard error of measurement (10.54 kPa), and did not impact the force production, since it increased only 1.4 % after the warmup. CONCLUSIONS: The results of this study suggest that the passive and active shear modulus responses of the individual hamstring muscles to static stretching are muscle-specific and that passive and active hamstring shear modulus are not changed by a standard warmup intervention.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".