Association between psychotropic medications and presence of sleep bruxism: A systematic review
Bibliographic record
Abstract
The purpose of this study was to systematically review the literature for studies that investigated the association between use of psychotropic medications and presence of sleep bruxism (SB). Observational studies were selected in a two-phase process. Searches were performed on six electronic databases, and a grey literature search was conducted on three databases. SB diagnosis was based on questionnaires or clinical examinations; no polysomnography examinations were performed. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. Overall quality of evidence was evaluated according to the Grading of Recommendations Assessment, Development and Evaluation criteria. Five analytical cross-sectional studies were included, evaluating antidepressants, anticonvulsants and psychostimulants. One study was judged as low risk of bias, three as moderate risk and one high risk. Antidepressants were evaluated in adult populations only; duloxetine (Odds Ratio [OR] = 2.16; 95% Confidence Interval [95% CI] = 1.12-4.17), paroxetine (OR = 3.63; 95% CI = 2.15-6.13) and venlafaxine (OR = 2.28; 95% CI = 1.34-3.86) were positively associated with SB risk. No increased odds of SB were observed considering use of citalopram, escitalopram, fluoxetine, mirtazapine and sertraline. With regard to anticonvulsants, only barbiturates were associated with SB in children (OR = 14.70; 95% CI = 1.85-116.90), while no increased odds were observed for benzodiazepine, carbamazepine and valproate. The only psychostimulant evaluated was methylphenidate, and an association with SB was observed in adolescents (OR = 1.67; 95% CI = 1.03-2.68). Findings from this SR suggested that medications such as duloxetine, paroxetine, venlafaxine, barbiturates and methylphenidate might be associated with SB; however, overall quality of evidence was considered very low, and therefore, caution is recommended.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".