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Record W2084320896 · doi:10.1177/0022034510373767

Cortical Orofacial Motor Representation

2010· article· en· W2084320896 on OpenAlexafffund
Limor Avivi‐Arber, Jye‐Chang Lee, Barry J. Sessle

Bibliographic record

VenueJournal of Dental Research · 2010
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity of Toronto
FundersOntario Ministry of Research and InnovationCanadian Institutes of Health ResearchResearch and Innovation Foundation
KeywordsTongueGenioglossusMotor cortexSomatosensory systemNeurosciencePrimary motor cortexAnatomyFacial musclesMicrostimulationNeuroplasticityPsychologyMedicineBiologyElectromyographyStimulationPathology

Abstract

fetched live from OpenAlex

Jaw and tongue motor alterations may occur following changes in food consistency, but whether such changes are associated with re-organization of motor representations within the facial sensorimotor cortex is unclear. We used intracortical microstimulation (ICMS) and recordings of evoked electromyographic responses to determine jaw (anterior digastric) and tongue (genioglossus) motor representations within the histologically defined face primary motor cortex (face-M1) and adjacent somatosensory cortex (face-S1) of rats fed hard (N = 6) or soft (N = 6) diet for 2 to 3 weeks. ICMS evoked jaw and tongue responses from an extensive area within the face-M1 and a smaller area within the face-S1. A significant contralateral predominance was reflected in the number and latency of ICMS-evoked jaw responses (p < 0.05). There were no significant differences between the hard- and soft-diet groups in jaw and tongue motor representations, suggesting that the rat's ability to adapt to changes in diet consistency may not be associated with significant neuroplasticity of sensorimotor cortex motor outputs.

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)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0030.000

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 head0.090
GPT teacher head0.479
Teacher spread0.388 · 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

Citations24
Published2010
Admission routes2
Has abstractyes

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