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Record W2473593875 · doi:10.1002/mus.25249

Interleaved neuromuscular electrical stimulation: Motor unit recruitment overlap

2016· article· en· W2473593875 on OpenAlexaff
Matheus Joner Wiest, Austin J. Bergquist, Helen L. Schimidt, Kelvin E. Jones, David F. Collins

Bibliographic record

VenueMuscle & Nerve · 2016
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsToronto Rehabilitation InstituteUniversity Health NetworkGeomechanica (Canada)University of Alberta
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCraig H. Neilsen Foundation
KeywordsTibialis anterior muscleStimulationCommon peroneal nerveStimulus (psychology)Physical medicine and rehabilitationTorqueNeuromuscular junctionMotor unitMedicineIntensity (physics)Biomedical engineeringAnatomyPhysicsNeurosciencePsychologySkeletal muscleInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: In this study, we quantified the "overlap" between motor units recruited by single pulses of neuromuscular electrical stimulation (NMES) delivered over the tibialis anterior muscle (mNMES) and the common peroneal nerve (nNMES). We then quantified the torque produced when pulses were alternated between the mNMES and nNMES sites at 40 Hz ("interleaved" NMES; iNMES). METHODS: Overlap was assessed by comparing torque produced by twitches evoked by mNMES, nNMES, and both delivered together, over a range of stimulus intensities. Trains of iNMES were delivered at the intensity that produced the lowest overlap. RESULTS: Overlap was lowest (5%) when twitches evoked by both mNMES and nNMES produced 10% peak twitch torque. iNMES delivered at this intensity generated 25% of maximal voluntary dorsiflexion torque (11 Nm). DISCUSSION: Low intensity iNMES leads to low overlap and produces torque that is functionally relevant to evoke dorsiflexion during walking. Muscle Nerve 55: 490-499, 2017.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.039
GPT teacher head0.251
Teacher spread0.211 · 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 designOther design
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

Citations12
Published2016
Admission routes1
Has abstractyes

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