Prevalence of bruxism in down syndrome patients: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Bruxism is a parafunctional activity characterised by grinding or clenching of teeth and is a common oral health concern in individuals with down syndrome (DS). Understanding the prevalence of bruxism in this population is crucial for developing effective management strategies. This systematic review and meta‐analysis is aimed to investigate the prevalence of bruxism among individuals with DS and explore its association with other oral health issues. Methods A comprehensive search was conducted across multiple electronic databases to identify relevant studies. Cross‐sectional and observational studies were included. Data on bruxism prevalence and associated factors were extracted, and a meta‐analysis was performed using both fixed‐effects (FE) and random‐effects (RE) models of MedCalc software. Heterogeneity among studies was assessed using I 2 statistics. New Castle‐Ottawa Scale was used to evaluate methodological quality of the included studies. Results Eight studies met the pre‐defined inclusion criteria and were included in the analysis. Seven studies used a questionnaire to assess bruxism. The pooled proportion estimate for occurrence of DS across the included studies was found to be 0.33 (95% CI: 0.22–0.45) as per the RE model and 0.35 (95% CI: 0.31–0.450) as per FE model in the quantitative analysis. All studies exhibited good methodological quality. Conclusion This systematic review and meta‐analysis provide evidence of a significant prevalence of bruxism among individuals with DS. The findings highlight the association of bruxism with other oral health issues and specific chromosomal abnormalities. Comprehensive oral health assessments, including diagnostic procedures like Polysomnography, are essential for addressing the unique oral health needs of individuals with DS. Further studies are recommended with a valid tool for the diagnosis. Early interventions and management strategies need to be tailored to this population, considering the multifaceted nature of oral health concerns in individuals with DS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".