Association between bruxism and temporomandibular disorders in children: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Bruxism in children and its relation to the development of temporomandibular disorders (TMD) has not been clearly determined yet. AIM: The objective of this systematic review was to evaluate the possible association between bruxism and TMD in children. DESIGN: Seven databases were searched, and 497 articles were assessed. Methodological quality was assessed through Newcastle-Ottawa Scale. The meta-analysis was performed with the articles in which extraction of data was possible and the summary effect measure through odds ratio (OR) and respective 95% confidence intervals (CIs). Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was used to assess the certainty of evidence. RESULTS: Ten cross-sectional studies were included in the systematic review. Of these, 8 showed a statistically significant association between bruxism and TMD. Seven studies however presented a high risk of bias. The meta-analysis was performed with 3 articles and obtained an OR of 2.97 (95% CI ranging from 1.72-5.15), indicating that children with bruxism are 2.97 times more likely to present TMD, with very low level of certainty defined by GRADE. CONCLUSIONS: Although the studies showed high risk of bias, the qualitative analysis of individual studies showed that the children with bruxism have greater chance of developing TMD.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".