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Record W2800951187 · doi:10.1139/apnm-2018-0080

Neuromuscular fatigue during repeated sprint exercise: underlying physiology and methodological considerations

2018· review· en· W2800951187 on OpenAlexaffvenue
Brandon W. Collins, Gregory E. P. Pearcey, Natasha C.M. Buckle, Kevin E. Power, Duane C. Button

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2018
Typereview
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of VictoriaMemorial University of Newfoundland
Fundersnot available
KeywordsSprintPhysical medicine and rehabilitationMuscle fatigueNeuromuscular diseaseMedicinePhysical therapyElectromyographyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.109
GPT teacher head0.329
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations57
Published2018
Admission routes2
Has abstractyes

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