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Record W1991455749 · doi:10.1002/mus.20074

Galvanic vestibular stimulation alters the onset of motor unit discharge

2004· article· en· W1991455749 on OpenAlexaff
Paul Kennedy, Andrew G. Cresswell, Romeo Chua, J. Timothy Inglis

Bibliographic record

VenueMuscle & Nerve · 2004
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGalvanic vestibular stimulationIsometric exerciseMotor unitStimulationReflexNeuroscienceVestibular systemSpinal cordStimulus (psychology)H-reflexElectromyographyMedicinePhysical medicine and rehabilitationAnatomyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

We have previously shown that galvanic vestibular stimulation (GVS) can modulate the amplitude of the passive soleus H-reflex. This study examined whether the response resulted from a general change in excitability of the motoneuron pool or a specific modulation of individual motor units (MUs). Subjects performed slow isometric plantarflexor actions in a prone lying and kneeling position until the discharge of a single gastrocnemius MU was detected. During randomly selected trials, a 1-mA bipolar, binaural galvanic stimulus was triggered just prior to the start of plantarflexor activity. With the knee extended and the medial gastrocnemius (MG) at a long muscle length, GVS did not have an effect on MU activity. However, when the knee was flexed and the MG muscle was shortened, GVS significantly modified the onset of activation and the initial firing frequency of MUs. This may reflect a change in the gain of the presynaptic inhibitory mechanisms that act on the motoneuron pool once a muscle reaches a shortened, nonoptimal force-producing length. Thus, GVS may provide an important research tool for activating descending vestibulospinal pathways that act on lower-limb motoneurons and may be applied to test the integrity of the spinal cord.

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 score0.960
Threshold uncertainty score0.368

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 head0.015
GPT teacher head0.223
Teacher spread0.209 · 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

Citations14
Published2004
Admission routes1
Has abstractyes

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