Is There Association Between Stress and Bruxism? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to investigate a possible association between stress and bruxism in humans. This study was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines under the code CRD42020188862, and the searches were performed on the following databases: PubMed, Scopus, Web of Science, Cochrane, LILACS, OpenGrey, and Google Scholar. This systematic review evaluated observational studies in adult humans with and without stress to verify the association between bruxism and the presence of stress. The risk of bias was evaluated through the Joanna Briggs Institute Critical Appraisal Tools for Analytical Cross-Sectional Studies. In quantitative analysis, the Odds Ratio (OR) and their 95% confidence interval (CI) were calculated through a fixed-effect model. Furthermore, a summary of the overall strength of evidence was presented using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE). A total of 1,458 studies were identified, and six were included in this systematic review. Two studies included were classified with a low risk of bias, and the others were classified with a moderate risk of bias. In three articles, a meta-analysis was performed and showed an association between these two factors (OR 2.07 [1.51, 2.83],p< 0.00001,I2= 45%). Besides that, a low certainty of the evidence was detected among this association. Stressed individuals show a higher chance of presenting bruxism when compared to healthy individuals. Despite the low heterogeneity found in the quantitative analysis among the articles reporting an association between stress and bruxism, further studies with similar methods are necessary to understand this relationship better.
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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 | 0.040 | 0.083 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.025 | 0.045 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".