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Record W2761408118 · doi:10.1249/mss.0000000000001445

Mechanisms of Fatigue and Recovery in Upper versus Lower Limbs in Men

2017· article· en· W2761408118 on OpenAlexaff
Gianluca Vernillo, John Temesi, Matthieu Martin, Guillaume Y. Millet

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2017
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPhysical medicine and rehabilitationPsychologyMedicine

Abstract

fetched live from OpenAlex

PURPOSE: To compare the mechanisms of fatigue and recovery between upper and lower limbs in the same subjects. METHODS: Twelve healthy young men performed a 2-min sustained maximal voluntary isometric contraction (MVC) of the knee extensors (KE) and on another day a 2-min MVC of the elbow flexors (EF). Neuromuscular function evaluations were performed with both transcranial magnetic and peripheral stimulations before (PRE), at the end of the 2-min MVC, and five more times within 8 min of recovery. RESULTS: Decreases in MVC and cortical voluntary activation were approximately 12% (P < 0.001) and approximately 25% greater (P = 0.04) in KE than EF at end of the 2-min MVC. Conversely, twitch response decreased approximately 29% more (P = 0.02) in EF than KE. Changes in motor-evoked potential with fatigue were not different between upper and lower limbs (P > 0.05), whereas the increase in silent period duration was approximately 30% greater in EF than KE (P < 0.05). CONCLUSIONS: Upper and lower limbs presented different magnitudes of total, central and peripheral fatigue. Total neuromuscular fatigue and central fatigue were greater in KE than EF. Conversely, peripheral fatigue and corticospinal inhibition were greater in EF than KE.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.264
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations55
Published2017
Admission routes1
Has abstractyes

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