A 2 week routine stretching programme did not prevent contraction‐induced injury in mouse muscle
Bibliographic record
Abstract
Most athletes stretch as part of their training regimen and it is commonly believed that this practice prevents muscle injury. We tested this belief using an animal model, in situ mouse extensor digitorum longus (EDL) muscle. One lower hindlimb was slowly stretched for 1 min on alternate days for 12 days; the other leg served as a control. The mouse was lightly anaesthetized during the stretching protocol (isofluorane). Both legs were tested in situ by measuring maximum isometric force and maximum work before and after an eccentric contraction that was designed to cause a contraction-induced injury. The difference between a contraction before and after (i.e. the deficit) was used as a measure of damage caused by the eccentric contraction. There was a threshold for force deficit at a peak to peak eccentric excursion amplitude of 19.5 % (i.e. L(o) +/- 9.75 %, where L(o) is muscle length at peak isometric force). There was a significant increase in force deficit, work deficit, and curve shift with an increase in eccentric excursion amplitude above the threshold. There was no statistical difference in the force deficit, work deficit, or curve shift between the stretched leg and the control leg (P > 0.05). A routine stretching programme, at least at the intensities employed in this experiment, did not prevent contraction-induced injury in the in situ mouse EDL muscle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".