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Record W2132750729 · doi:10.1002/mus.21275

Modulation of the soleus H‐reflex following galvanic vestibular stimulation and cutaneous stimulation in prone human subjects

2009· article· en· W2132750729 on OpenAlexaff
Catherine R. Lowrey, Leah R. Bent

Bibliographic record

VenueMuscle & Nerve · 2009
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGalvanic vestibular stimulationStimulationVestibular systemSomatosensory systemH-reflexReflexCutaneous nerveStimulus (psychology)NeuroscienceVestibular nerveSoleus muscleConditioningMedicineSensory systemPsychologyAnatomySkeletal muscle

Abstract

fetched live from OpenAlex

There is evidence to suggest that vestibular and somatosensory inputs may interact when they are processed by the central nervous system, although the nature of the individual sensory contributions to this interaction is unknown. We examined the effects of a combined vestibular and cutaneous conditioning stimulus on the motoneuron pool that supplies the soleus muscle via the Hoffman reflex (H-reflex). We applied galvanic vestibular stimulation (GVS; bipolar, binaural, 500 ms, 2.5-mA square-wave pulse) and cutaneous stimulation (medial plantar nerve; 11 ms, three-pulse train, 200 HZ) to prone human subjects and examined changes in the amplitude of the H-reflex. GVS alone caused facilitation (approximately 20%) of the H-reflex, whereas ipsilateral cutaneous stimulation alone caused a 26% inhibition. Paired GVS and cutaneous stimulation resulted in a linear summation of the individual conditioning effects. H-reflex amplitudes observed after paired conditioning with GVS and cutaneous stimulation could be predicted from the amplitudes observed with individual conditioning. These results suggest that in the prone position, when the muscles are not posturally engaged, vestibular and somatosensory information appear to sum in a linear fashion to influence the reflex response of lower limb motoneurons. Muscle Nerve 40: 213-220, 2009.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.528

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 head0.022
GPT teacher head0.273
Teacher spread0.251 · 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 designBench or experimental
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

Citations31
Published2009
Admission routes1
Has abstractyes

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