MétaCan
Menu
Back to cohort
Record W2593057769 · doi:10.1002/mus.25634

Interleaved neuromuscular electrical stimulation after spinal cord injury

2017· article· en· W2593057769 on OpenAlexaff
Austin J. Bergquist, Matheus Joner Wiest, Yoshino Okuma, David F. Collins

Bibliographic record

VenueMuscle & Nerve · 2017
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of AlbertaToronto Rehabilitation InstituteUniversity Health Network
Fundersnot available
KeywordsMedicineElectromyographyTrunkStimulationSpinal cord injuryPhysical medicine and rehabilitationSpinal cordAnesthesiaPlantar flexionFunctional electrical stimulationParalysisPhysical therapyAnatomySurgeryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Neuromuscular electrical stimulation (NMES) over a muscle belly (mNMES) recruits superficial motor units (MUs) preferentially, whereas NMES over a nerve trunk (nNMES) recruits MUs evenly throughout the muscle. We performed tests to determine whether "interleaving" pulses between the mNMES and nNMES sites (iNMES) reduces the fatigability of contractions for people experiencing paralysis because of chronic spinal cord injury. METHODS: Plantar flexion torque and soleus electromyography (M-waves) were recorded from 8 participants. A fatigue protocol (75 contractions; 2 s on/2 s off for 5 min) was delivered by iNMES. The results were compared with previously published data collected with mNMES and nNMES in the same 8 participants. RESULTS: Torque declined ∼40% more during mNMES than during nNMES or iNMES. M-waves declined during mNMES but not during nNMES or iNMES. DISCUSSION: To reduce fatigability of electrically evoked contractions of paralyzed plantar flexors, iNMES is equivalent to nNMES, and both are superior to mNMES. Muscle Nerve 56: 989-993, 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.746

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 head0.017
GPT teacher head0.265
Teacher spread0.248 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueMuscle & NerveSame topicMuscle activation and electromyography studiesFrench-language works237,207