PASSIVE STRETCHING DOES NOT PROTECT AGAINST ACUTE CONTRACTION-INDUCED INJURY IN MOUSE EDL MUSCLE
Bibliographic record
Abstract
A popular part of many athletes pre-game regime is to stretch. We examined whether a pre-injury stretching protocol could prevent acute contraction-induced injury. The in-situ EDL (extensor digitorum longus) muscle of an anesthetized mouse (80 mg/kg IP) was used. Damage to the muscle from eccentric contraction-induced injury was quantified by the deficit in tetanic force production and was not confounded by metabolic fatigue. The force deficits resulting from eccentric contractions alone (E) were compared with the force deficits resulting from a protocol that consisted of a stretch before the eccentric contractions (S+E). The pre-injury stretch was performed to 5% Lo strain, at a velocity of 0.5 mm/s. The muscle was held in the stretch position for 1 min, then slowly released. The eccentric contraction protocols (excursion ≥ 24% Lo) resulted in pronounced force deficits that increased with the excursion amplitude of the eccentric contraction. The eccentric contractions also resulted in an average right shift of 2 ± 0.53% in the length-force relationship. The pre-injury stretch protocol did not reduce the force deficit due to contraction-induced injury. Supported by NSERC
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".