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Record W2919735513 · doi:10.1002/mus.26460

Functional electrical stimulation following nerve injury in a large animal model

2019· article· en· W2919735513 on OpenAlexaff
Marta Cercone, Jonathan C. Jarvis, Norm G. Ducharme, Justin Perkins, Richard J. Piercy, Michael P. Willand, Lisa M. Mitchell, Michael Sledziona, L. V. SODERHOLM, Jonathan Cheetham

Bibliographic record

VenueMuscle & Nerve · 2019
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsFunctional electrical stimulationAnimal modelStimulationMedicineNerve injuryNeuroscienceElectric stimulationNerve stimulationAnesthesiaPhysical medicine and rehabilitationPsychologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Controversy exists over the effects of functional electrical stimulation (FES) on reinnervation. We hypothesized that intramuscular FES would not delay reinnervation after recurrent laryngeal nerve (RLn) axonotmesis. METHODS: RLn cryo-injury and electrode implantation in ipsilateral posterior cricoarytenoid muscle (PCA) were performed in horses. PCA was stimulated for 20 weeks in eight animals; seven served as controls. Reinnervation was monitored through muscle response to hypercapnia, electrical stimulation and exercise. Ultimately, muscle fiber type proportions and minimum fiber diameters, and RLn axon number and degree of myelination were determined. RESULTS: Laryngeal function returned to normal in both groups within 22 weeks. FES improved muscle strength and geometry, and induced increased type I:II fiber proportion (p = 0.038) in the stimulated PCA. FES showed no deleterious effects on reinnervation. DISCUSSION: Intramuscular electrical stimulation did not delay PCA reinnervation after axonotmesis. FES can represent a supportive treatment to promote laryngeal functional recovery after RLn injury. Muscle Nerve 59:717-725, 2019.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.534

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 head0.018
GPT teacher head0.276
Teacher spread0.258 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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