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Effects of transcutaneous electrical nerve stimulation on H-reflex and spinal spasticity

2019· article· en· W123466872 on OpenAlexaff
C. Goulet, André Arsenault, Daniel Bourbonnais, Marie-Thérèse Laramée, Yves Lepage

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsCanadian Physiotherapy Association
Fundersnot available
KeywordsSpasticityClonusTranscutaneous electrical nerve stimulationMedicineTriceps reflexAnkle jerk reflexH-reflexReflexSpinal cord injuryAnesthesiaAchilles tendonStimulationPhysical medicine and rehabilitationSpinal cordWithdrawal reflexTendonAnatomyInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the short-term effects of transcutaneous electrical nerve stimulation (TENS; 99 Hz; 250 ms pulses) on H-reflex and spinal spasticity. Considering the reflex hyperexcitability commonly displayed in spinal cord-injured subjects, it was hypothesized that repetitive low threshold afferent stimulation would have an inhibitory effect on the triceps surae H-reflexes which could also be reflected by a decrease in plantarflexor spasticity. Clonus, Achilles tendon reflex and modified Ashworth evaluations were performed on 14 spinal cord-injured subjects prior to and after 30 minutes' application of TENS. Non-parametric statistical analyses (n = 14; alpha = 0.05) failed to reveal significant effects of TENS on H-reflex amplitude. However, there was a significant decrease in scores for the Achilles tendon reflex and the modified Ashworth test. The clonus score decreased in most subjects post-TENS, although not in a statistically significant manner. The present pilot results thus suggest that TENS appears to be effective in reducing spinal spasticity, as measured clinically.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.006
GPT teacher head0.277
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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

Citations65
Published2019
Admission routes1
Has abstractyes

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