Association Between Occlusal Wear Facets and Undiagnosed Sleep Bruxism in Adult Dental Patients
Bibliographic record
Abstract
Background: Teeth grinding can occur during sleep and may remain unknown due to the absence of overt symptoms and lack of conscious awareness. Over time, repetitive actions can subtly alter the physical appearance of the teeth's tops, either in terms of appearance or texture. People might not be aware of these slight variations, but they might allude to other nighttime practices. The study examines the relationship between occlusal wear facets and undiagnosed sleep bruxism in adult dental patients, focusing on how tooth wear can serve as a clinically predictive indicator. Methods: The cross-sectional study was conducted between May 2025 and January 2026 within the dental clinics of Islamabad, using convenience sampling. A sample of 385 adult patients who completed a self-report Oral Behaviour Checklist (OBC) and a Tooth Wear Index (TWI) were administered the clinical assessments. The statistical analysis of the data was conducted using SPSS Version 26, which included descriptive statistics, t-tests, ANOVA, Pearson correlation, and linear regression to determine the correlations between oral behaviours and occlusal wear. Results: Out of the 385 respondents, 194 (50%) were male and 191 (50%) were female. Both the Oral Behaviour Checklist (OBC) and the Tooth Wear Index (TWI) showed higher scores in males than in females (p < 0.01) There was consequently a significant positive correlation between tooth wear scores and oral behaviour ( r = 0.312 p < 0.001 ) Another critical variable was age, where older adults showed higher scores in both indices (p < 0.01) . Linear regression analysis revealed that scores on OBC were highly predictive of scores on TWI ( B = 0.94 , p < 0.001 ). Caffeine use and smoking status had a significant correlation with specific health-related issues, including the presence of TMJ issues and the frequency of headaches ( p < 0.001 ). Conclusion: These results indicate that self-reported oral behaviours are strongly related to occlusal wear, suggesting that tooth wear could be a clinical marker of undiagnosed sleep bruxism. Implementing regular occlusal wear screening during dental facility visits may help identify and treat dental issues at an earlier stage, thereby preventing long-term dental damage.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".