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Record W2101375200 · doi:10.1177/154405910708600906

Identification of a Sleep Bruxism Subgroup with a Higher Risk of Pain

2007· article· en· W2101375200 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Dental Research · 2007
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de Montréal
FundersCanadian Institutes of Health Research
KeywordsSleep BruxismSleep (system call)Masticatory forceOdds ratioMedicineOrofacial painPsychologyPhysical therapyPsychiatryDentistryInternal medicineElectromyography

Abstract

fetched live from OpenAlex

Sleep bruxism research diagnostic criteria (SB-RDC) have been applied since 1996. This study was performed to validate these criteria and to challenge the hypothesis that pain is associated with lower frequencies of orofacial activities. Polygraphic recordings were made of 100 individuals presenting with a clinical diagnosis of sleep bruxism and 43 control individuals. TwoStep Cluster analyses (SPSS) were performed with sleep bruxism variables to reveal groupings among sleep bruxers and control individuals. Participants completed questionnaires during screening, diagnosis, and recording sessions. Cluster analysis identified three subgroups of sleep bruxers. Interestingly, 45 of the 46 sleep bruxers with values below SB-RDC were classified in the low-frequency cluster. These individuals were more likely to complain of pain and fatigue of masticatory muscles than were the higher-frequency sleep bruxers (odds ratios > 3.9, p < 0.01). Sleep bruxers were distributed among three heterogeneous groups. Sleep bruxers with low frequencies of orofacial activities were more at risk of reporting pain.

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.

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)0.024
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.044
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.069
GPT teacher head0.471
Teacher spread0.402 · 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