Neuromuscular fatigue during repeated sprint exercise: underlying physiology and methodological considerations
Bibliographic record
Abstract
Neuromuscular fatigue occurs when an individual's capacity to produce force or power is impaired. Repeated sprint exercise requires an individual to physically exert themselves at near-maximal to maximal capacity for multiple short-duration bouts, is extremely taxing on the neuromuscular system, and consequently leads to the rapid development of neuromuscular fatigue. During repeated sprint exercise the development of neuromuscular fatigue is underlined by a combination of central and peripheral fatigue. However, there are a number of methodological considerations that complicate the quantification of the development of neuromuscular fatigue. The main goal of this review is to synthesize the results from recent investigations on the development of neuromuscular fatigue during repeated sprint exercise. Hence, we summarize the overall development of neuromuscular fatigue, explain how recovery time may alter the development of neuromuscular fatigue, outline the contributions of peripheral and central fatigue to neuromuscular fatigue, and provide some methodological considerations for quantifying neuromuscular fatigue during repeated sprint exercise.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".