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Record W2340106273 · doi:10.1080/08869634.2015.1106811

Impact of sleep bruxism on masseter and temporalis muscles and bite force

2016· article· en· W2340106273 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.

Bibliographic record

VenueCRANIO® · 2016
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBite force quotientMasticatory forceSleep BruxismElectromyographyMasseter muscleMasticationMolarPolysomnographyTemporal muscleOrthodonticsMedicineDentistryPhysical medicine and rehabilitationAnesthesia

Abstract

fetched live from OpenAlex

OBJECTIVES: This study aimed to analyze the impact of sleep bruxism (SB) on electromyography (EMG) activity and the thickness of the masseter and temporal and maximal molar bite force. METHOD: Ninety individuals, aged between 18 and 45 years, were selected and divided into two groups: Group I (case group, 45 individuals with SB) and Group II (control group, 45 individuals without SB). A diagnosis of SB was made from polysomnography. RESULTS: The data obtained from EMG and the muscle thickness and the maximal molar bite force were tabulated (SPSS 21.0), normalized, and subjected to statistical analysis (p ≤ 0.05). Comparisons between the groups showed significant differences regarding the habitual chewing of hard food for the left temporalis muscle (p = 0.04) and the chewing of soft food for the right masseter muscle (p = 0.04), but no significant differences for the measurements of muscle thickness and maximal molar bite force. DISCUSSION: The present data suggest that SB negatively altered the masticatory muscles' functions. Based on the results of this research, it can be concluded that individuals with SB showed decreased EMG activity in the masticatory muscles.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.047
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.034
GPT teacher head0.392
Teacher spread0.359 · 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