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Record W2100750133 · doi:10.1177/154405910308200408

Evidence that Experimentally Induced Sleep Bruxism is a Consequence of Transient Arousal

2003· article· en· W2100750133 on OpenAlexaff
Takafumi Kato, J. Montplaisir, F. Guitard, Barry J. Sessle, James P. Lund, Gilles Lavigne

Bibliographic record

VenueJournal of Dental Research · 2003
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsMcGill UniversityUniversité de MontréalHôpital du Sacré-Cœur de MontréalUniversity of Toronto
Fundersnot available
KeywordsArousalSleep BruxismSleep (system call)MedicineAnesthesiaPsychologyElectromyographyMuscle toneHeart rateAudiologyInternal medicinePhysical medicine and rehabilitationNeuroscienceBlood pressure

Abstract

fetched live from OpenAlex

Spontaneous rhythmic masticatory muscle activity (RMMA) during sleep occurs more frequently following spontaneous transient micro-arousal in patients with sleep bruxism (SB) and normal controls. Here, we tested the hypothesis that an experimental arousal would be followed by an increase in RMMA. We identified RMMA on polygraphic recordings taken before and after sensory stimulation to induce experimental arousal in eight SB patients and eight matched normal subjects. The rate of experimental arousal and the level of resting electromyographic activity in masseter and suprahyoid muscles during sleep did not differ between the groups. In both, muscle tone and heart rate increased during the experimental arousal. Although post-arousal RMMA occurred in all SB patients, it was seen in only one normal subject. Moreover, tooth-grinding occurred during 71% of the evoked RMMA in SB patients. These results support the hypothesis that SB is an exaggerated form of oromotor activity associated with sleep 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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.464
GPT teacher head0.559
Teacher spread0.095 · 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 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

Citations227
Published2003
Admission routes1
Has abstractyes

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