Sleep Bruxism: Mapping Potential Direct and Indirect Risk Pathways in EPISONO Adult Population‐Based Study
Bibliographic record
Abstract
AIM: To explore the direct and indirect pathways through which sociodemographic, psychological, behavioural, and clinical factors influence sleep bruxism (SB). METHODS: This cross-sectional study was conducted with a sample of 686 adults (mean age of 50.1 years; 380 female and 306 male), from a total of 712 individuals from the Sao Paulo Epidemiological Sleep Study (EPISONO) follow performed in 2015. SB was assessed using self-report, overnight polysomnography (PSG-based), and combined methods. Sociodemographic, psychological, behavioural and clinical factors were assessed. Structural Equation Modelling was used to examine the pathways between potential risk factors and SB. RESULTS: From an initial sample of 1042, 712 returned for follow-up and 686 individuals were eligible based on the SB outcomes evaluated and having undergone PSG. The SB self-reported prevalence was 17.1%, 30.5% presented PSG-based SB and 7.4% in combination of methods (self-report+PSG). Sleep bruxism (assessed by all methods) was directly associated with higher levels of insomnia and younger age. Higher socioeconomic status was directly associated with self-reported SB, whereas PSG-based and self-report+PSG SB were associated with increased obstructive sleep apnea and smoking. Regarding indirect effects, elevated anxiety and depressive symptoms indirectly impacted all forms of SB via increased insomnia levels. CONCLUSIONS: Our findings highlight distinct and overlapping pathways of SB. Insomnia and younger age consistently predicted SB, while psychological factors indirectly impacted SB via insomnia. Demographic, behavioural, and clinical factors showed direct associations that varied according to the assessment method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".