Neuromuscular fatigue recovery following rapid and slow stretch–shortening cycle movements
Bibliographic record
Abstract
The purpose of this study was to investigate underlying mechanisms and neuromuscular recovery patterns following rapid and slow stretch-shortening cycle (SSC) movements performed to fatigue. Fourteen (10 moderately trained (MT) and four highly trained (HT)) subjects completed rapid and slow SSC movements to fatigue. The rapid SSC movement consisted of continuous drop jumps from a 30 cm platform until a predetermined jump height was no longer maintained, and the slow SSC movement consisted of continuous squats to 90° of knee flexion at a load of 65% of subject's one-repetition maximum until no further repetitions could be completed. Although blood lactate measures were significantly (p < 0.002) higher after the rapid SSC condition versus after the slow SSC condition, the recovery of neuromuscular properties (maximum voluntary contractions, twitch force, muscle compound action potential) following the two conditions to fatigue did not differ. The duration of the rapid SSC movement was dependent on the training status of the subject; HT subjects performed the rapid SSC longer (68.2%) than the MT subjects until fatigued. Thus, the neuromuscular fatigue recovery patterns were independent of the type of SSC movement, condition duration, and subject training status. Because rapid and slow SSC exercises induce similar fatigue patterns, training programs incorporating rapid SSC exercises can be developed similar to that prescribed in traditional slow SSC resistance training programs.
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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) | 0.001 | 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".