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Record W1596081110 · doi:10.1002/mus.24092

Anconeus motor unit number estimates using decomposition‐based quantitative electromyography

2013· article· en· W1596081110 on OpenAlexafffund
Daniel E. Stevens, Brad Harwood, Geoffrey A. Power, Timothy J. Doherty, Charles L. Rice

Bibliographic record

VenueMuscle & Nerve · 2013
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of CalgaryWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMotor unitElectromyographyElbowElbow flexionRoot mean squareMathematicsPhysical medicine and rehabilitationAnatomyMedicinePhysics

Abstract

fetched live from OpenAlex

INTRODUCTION: Motor unit number estimates (MUNEs) provide important information in health, aging, and disease, and can be determined using decomposition-enhanced spike-triggered averaging (DE-STA). Discrimination of surface-detected motor unit potentials (S-MUPs) has been limited to contractile forces of ∽30% maximum voluntary contraction (MVC), which is insufficient to recruit a representative sample of the entire MU pool in most muscles. Unique features of the anconeus may permit MUNEs at high muscle activation levels. METHODS: In 10 men (25 ± 3 years), anconeus MUNEs were performed using DE-STA at 10%, 30%, and 50% root-mean-square of MVC (RMS(MVC)). RESULTS: The mean compound muscle action potential of the anconeus was ∽6 mV, and average S-MUP amplitudes were ∽100 μV, 145 μV, and 235 μV at 10%, 30%, and 50% RMS(MVC), resulting in low average MUNEs of 58, 38, and 25, respectively. CONCLUSIONS: Elbow extensor force-EMG relationships suggest full recruitment of the anconeus MU pool at 50% RMS(MVC), thus providing a representative sample for MUNE.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score1.000

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 head0.023
GPT teacher head0.274
Teacher spread0.251 · 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 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

Citations19
Published2013
Admission routes2
Has abstractyes

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