Effect of altering starting length and activation timing of muscle on fiber strain and muscle damage
Bibliographic record
Abstract
Muscle strain injuries are some of the most frequent injuries in sports and command a great deal of attention in an effort to understand their etiology. These injuries may be the culmination of a series of subcellular events accumulated through repetitive lengthening (eccentric) contractions during exercise, and they may be influenced by a variety of variables including fiber strain magnitude, peak joint torque, and starting muscle length. To assess the influence of these variables on muscle injury magnitude in vivo, we measured fiber dynamics and joint torque production during repeated stretch-shortening cycles in the rabbit tibialis anterior muscle, at short and long muscle lengths, while varying the timing of activation before muscle stretch. We found that a muscle subjected to repeated stretch-shortening cycles of constant muscle-tendon unit excursion exhibits significantly different joint torque and fiber strains when the timing of activation or starting muscle length is changed. In particular, measures of fiber strain and muscle injury were significantly increased by altering activation timing and increasing the starting length of the muscle. However, we observed differential effects on peak joint torque during the cyclic stretch-shortening exercise, as increasing the starting length of the muscle did not increase torque production. We conclude that altering activation timing and muscle length before stretch may influence muscle injury by significantly increasing fiber strain magnitude and that fiber dynamics is a more important variable than muscle-tendon unit dynamics and torque production in influencing the magnitude of muscle injury.
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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 | 0.001 |
| 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) | 0.002 | 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".