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Record W2144078437 · doi:10.1123/mc.2014-0022

Premovement Changes in Corticospinal Excitability of the Biceps Brachii are Not Different Between Arm Cycling and an Intensity-Matched Tonic Contraction

2014· article· en· W2144078437 on OpenAlexaff
David B. Copithorne, Davis A. Forman, Kevin E. Power

Bibliographic record

VenueMotor Control · 2014
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBicepsTonic (physiology)ElectromyographyNeuroscienceCyclingTranscranial magnetic stimulationContraction (grammar)Muscle contractionPhysical medicine and rehabilitationPsychologyStimulationMedicineAnatomyInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to determine if supraspinal and/or spinal motoneuron excitability of the biceps brachii were differentially modulated before: 1) arm cycling and 2) an intensity-matched tonic contraction. Surface EMG recordings of motor evoked potentials (MEPs) and cervicomedullary motor evoked potentials (CMEPs) were used to assess supraspinal and spinal motoneuron excitability, respectively. MEP amplitudes were larger and onset latencies shorter, before arm cycling and tonic contraction when compared with rest with no intent to move, but with no difference between motor outputs. CMEP amplitudes and onset latencies remained unchanged before cycling and tonic contraction compared with rest. Premovement enhancement of corticospinal excitability was due to an increase in supraspinal excitability that was not task-dependent. This suggests that a common neural drive is used to initiate both motor outputs with task-dependent changes in neural excitability only being evident once the motor outputs have begun.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations23
Published2014
Admission routes1
Has abstractyes

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