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Record W2017654906 · doi:10.1682/jrrd.2005.02.0041

Effectiveness of muscle vibration in modulating corticospinal excitability

2005· article· en· W2017654906 on OpenAlexaff
Lorraine Smith, Brenda Brouwer

Bibliographic record

VenueThe Journal of Rehabilitation Research and Development · 2005
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsTranscranial magnetic stimulationExtensor Carpi UlnarisFlexor Carpi UlnarisStimulus (psychology)ForearmNeuroscienceMedicinePhysical medicine and rehabilitationSilent periodMotor cortexStimulationAudiologyPsychologyAnatomyUlnar nerveElbow

Abstract

fetched live from OpenAlex

This study explored the effect of vibration of the forearm extensors on motor cortical excitability and the influence of stimulus duration. Sixteen healthy volunteers between 23 and 42 years old participated in one or two studies. We applied 15 or 30 min of 100 Hz, 0.5 mm-amplitude vibration to the extensor carpi radialis longus (ECRL) muscle. Cortical excitability was measured as the magnitude of the motor-evoked potentials (MEPs), and the size of the representation area associated with ECRL and flexor carpi ulnaris muscles was determined with the use of transcranial magnetic stimulation. A 33% increase in MEP size and enlarged area of cortical excitability was detected 5 min after 15 min of vibration in the ECRL only. No changes were associated with 30 min of vibration in either muscle. These findings indicate that the facilitatory effects of vibration in healthy subjects depend on stimulus duration and provide impetus for testing the extent to which short-duration vibration augments corticospinal excitability to improve muscle function in people with central motor disorders.

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 score0.007

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 head0.025
GPT teacher head0.302
Teacher spread0.277 · 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

Citations34
Published2005
Admission routes1
Has abstractyes

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