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Record W1987078357 · doi:10.5430/jbgc.v5n1p28

The influence of bite force strength on brain activity: A functional magnetic resonance imaging study

2015· article· en· W1987078357 on OpenAlexvenueno aff
Takeo Kanayama, Hiroyuki Miyamoto, Atsuro Yokoyama, Toshiyuki Takahashi, Yasuyuki Shibuya

Bibliographic record

VenueJournal of Biomedical Graphics and Computing · 2015
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFunctional magnetic resonance imagingMagnetic resonance imagingNuclear magnetic resonanceMedicineMaterials sciencePsychologyNeurosciencePhysicsRadiology

Abstract

fetched live from OpenAlex

In recent years, functional magnetic resonance imaging has been used to determine the interaction between chewing and brainactivity. However, the factors influencing the activity of the motor cortex have not been fully elucidated. Therefore, the presentstudy investigated the influence of the magnitude of bite force on brain activity. Fifteen right-handed healthy subjects (24-32 years; mean age, 27.8 years) were included. Sustained, constant clenching with small and large forces comprised the motortask. The spatial extent of the functional magnetic resonance imaging signal in the primary sensorimotor cortex increased withan increasing bite force in all subjects. These findings indicated the possibility of measuring the activated area in the primarysensorimotor cortex during clenching using functional magnetic resonance imaging, which revealed that the brain activity wasrelated to the magnitude of the bite force.

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 score0.005

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 head0.032
GPT teacher head0.284
Teacher spread0.252 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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