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Record W1946975444 · doi:10.1002/mus.24144

H‐reflexes reduce fatigue of evoked contractions after spinal cord injury

2013· article· en· W1946975444 on OpenAlexaff
Austin J. Bergquist, Matheus Joner Wiest, Yoshino Okuma, David F. Collins

Bibliographic record

VenueMuscle & Nerve · 2013
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineReflexElectromyographySpinal cord injurySpinal cordMuscle fatigueTrunkPhysical medicine and rehabilitationH-reflexStimulationMuscle contractionContraction (grammar)AnesthesiaAnatomyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Neuromuscular electrical stimulation (NMES) over a muscle belly (mNMES) generates contractions predominantly through M-waves, while NMES over a nerve trunk (nNMES) can generate contractions through H-reflexes in people who are neurologically intact. We tested whether the differences between mNMES and nNMES are present in people with chronic motor-complete spinal cord injury and, if so, whether they influence contraction fatigue. METHODS: Plantar-flexion torque and soleus electromyography were recorded from 8 participants. Fatigue protocols were delivered using mNMES and nNMES on separate days. RESULTS: nNMES generated contractions that fatigued less than mNMES. Torque decreased the least when nNMES generated contractions, at least partly through H-reflexes (n = 4 participants; 39% decrease), and torque decreased the most when contractions were generated through M-waves, regardless of NMES site (nNMES 71% decrease, n = 4; mNMES, 73% decrease, n = 8). CONCLUSIONS: nNMES generates contractions that fatigue less than mNMES, but only when H-reflexes contribute to the evoked contractions.

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 score0.945
Threshold uncertainty score0.586

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 head0.023
GPT teacher head0.275
Teacher spread0.252 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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