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Record W1970621674 · doi:10.1177/00220345010800101501

Sleep Bruxism: An Oromotor Activity Secondary to Micro-arousal

2001· article· en· W1970621674 on OpenAlexafffund
Takafumi Kato, Pierre Rompré, Jacques Montplaisir, Barry J. Sessle, Gilles Lavigne

Bibliographic record

VenueJournal of Dental Research · 2001
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsUniversity of TorontoUniversité de MontréalHôpital du Sacré-Cœur de Montréal
FundersCanadian Institutes of Health Research
KeywordsArousalSleep BruxismElectroencephalographyMedicineElectromyographyWakefulnessAnesthesiaSleep (system call)Autonomic nervous systemHeart ratePsychologyCardiologyNeuroscienceInternal medicinePhysical medicine and rehabilitationBlood pressure

Abstract

fetched live from OpenAlex

Spontaneous rhythmic masticatory muscle activity (RMMA) during sleep occurs in relation to transient activation in the cerebral and autonomic nervous systems of normal subjects and in patients with sleep bruxism (SB). In this study, we made a quantitative assessment of the sequential changes in cortical electroencephalographic (EEG) and autonomic-cardiac activities associated with micro-arousals preceding RMMA episodes. We matched 10 SB patients with 10 normal subjects. The onset of RMMA episodes was defined in terms of the onset of activation in the suprahyoid muscles. In SB patients, an increase in cortical EEG activity was observed 4 seconds before the onset of suprahyoid activity in 79% of episodes. A significant acceleration in heart rate was initiated one cardiac cycle before RMMA onset. A clear sequence of cortical to autonomic-cardiac activation precedes jaw motor activity in SB patients. This suggests that SB is a powerful oromotor manifestation secondary to micro-arousal.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.150
GPT teacher head0.530
Teacher spread0.380 · 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

Citations312
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Dental ResearchSame topicTemporomandibular Joint DisordersFrench-language works237,207