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Record W2148615842 · doi:10.1123/mcj.17.1.62

Subthreshold Vestibular Reflex Effects in Seated Humans Can Contribute to Soleus Activation When Combined With Cutaneous Inputs

2013· article· en· W2148615842 on OpenAlexafffund
Kristen E Thomas, Leah R. Bent

Bibliographic record

VenueMotor Control · 2013
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVestibular systemGalvanic vestibular stimulationSensory systemReflexIsometric exerciseStimulationSomatosensory systemMedicineNeurosciencePhysical medicine and rehabilitationSoleus muscleStimulus modalityH-reflexAnatomyPsychologyInternal medicineSkeletal muscle

Abstract

fetched live from OpenAlex

The integration of vestibular and somatosensory information for the control of lower limb musculature remains elusive. To determine whether a subthreshold vestibular input influences the cutaneous evoked response, the isometric EMG activity in the posturally inactive soleus muscles of 13 healthy, seated subjects was collected. Vestibular afferents were activated using galvanic vestibular stimulation (GVS; 1.8-2.5mA, 500ms), while percutaneous electrical stimulation was delivered to the distal tibial nerve (11ms train of 3 × 1.0 ms pulses, 200Hz) to activate foot sole skin afferents. GVS elicited responses in soleus both independently and when combined with cutaneous stimulation. The responses to the combined sensory input showed an interaction between the two sensory modalities to influence muscle activation. Of note is the presence of significant muscle modulation in the combined condition, where subthreshold vestibular inputs altered the outcome of the cutaneous reflex response. This finding has implications for individuals with sensory deficiency. In the case of an absent or deficient sensory modality, balance protective reflexes to maintain postural equilibrium may be enhanced with targeted sensory augmentation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.217
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.

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

Citations9
Published2013
Admission routes2
Has abstractyes

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