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Record W2623140787 · doi:10.1002/mus.25688

Is the firing rate of motor units in different vastus medialis regions modulated similarly during isometric contractions?

2017· article· en· W2623140787 on OpenAlexaff
Hélio V. Cabral, Leonardo Mendes Leal de Souza, Roger Gomes Tavares Mello, Alessio Gallina, Líliam Fernandes de Oliveira, Taian Vieira

Bibliographic record

VenueMuscle & Nerve · 2017
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of British Columbia
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsIsometric exerciseVastus medialisMotor unitElectromyographyAnatomyMedicinePhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Previous evidence suggests the fibers of different motor units reside within distinct vastus medialis (VM) regions. It remains unknown whether the activity of these motor units may be modulated differently. Herein we assess the discharge rate of motor units detected proximodistally from the VM to address this issue. METHODS: Surface electromyograms (EMGs) were recorded proximally and distally from the VM while 10 healthy subjects performed isometric contractions. Single motor units were decomposed from surface EMGs. The smoothed discharge rates of motor units identified from the same and from different VM regions were then cross-correlated. RESULTS: During low-level contractions, the discharge rate varied more similarly for distal (cross-correlation peak; interquartile interval: 0.27-0.40) and proximal (0.28-0.52) than for proximodistal pairs of VM motor units (0.20-0.33; P = 0.006). DISCUSSION: The discharge rates of motor units from different proximodistal VM regions show less similarity in their variations than those of pairs of units either distally or proximally. Muscle Nerve 57: 279-286, 2018.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.239
Teacher spread0.207 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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