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Record W2186024102 · doi:10.1111/sms.12596

Unilateral elbow flexion fatigue modulates corticospinal responsiveness in non‐fatigued contralateral biceps brachii

2015· article· en· W2186024102 on OpenAlexafffund
Saied Jalal Aboodarda, Nemanja Šambaher, David G. Behm

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2015
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBicepsIsometric exerciseMedicineTranscranial magnetic stimulationMuscle fatiguePhysical medicine and rehabilitationElbow flexionElectromyographyElbowStimulationCardiologyAnesthesiaPhysical therapyInternal medicineAnatomy

Abstract

fetched live from OpenAlex

Exercise-induced fatigue can change motor performance in non-exercised muscles. The objective was to investigate unilateral elbow flexion (EF) fatigue effects on the maximal voluntary force (MVC) and corticospinal excitability of contralateral non-exercised biceps brachii (BB). Transcranial magnetic, transmastoid electrical, and brachial plexus electrical stimulation were used to elicit motor evoked potentials (MEP), cervicomedullary motor evoked potentials (CMEP), and compound muscle action potentials in the contralateral non-exercised BB of 12 participants before and after (i) two bouts of 100-s unilateral EF (fatigue) or (ii) control. Three stimuli were evoked every 1.5 s during a series of 6-s isometric EF at 100%, 50%, and 5% of MVC. The non-exercised EF MVC force, electromyographic activity, and voluntary activation were not significantly different between fatigue and control. Non-exercised BB MEP and CMEP amplitudes during 100% MVCs demonstrated significantly higher (P = 0.03) and lower values (P = 0.01), respectively, after fatigue compared with control. There was no difference between the two conditions for MEP and CMEP amplitudes during 50% and 5% MVCs. Unilateral exercise-induced EF fatigue did not lead to cross-over central fatigue to the contralateral homologous muscle but enhanced the supraspinal responsiveness (MEP/CMEP) of the neural circuitries supplying central commands to non-exercised muscles at higher contraction intensity.

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 score0.040
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.287
Teacher spread0.258 · 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

Citations40
Published2015
Admission routes2
Has abstractyes

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