Sleep bruxism in children and adolescents—A scoping review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: This review paper focuses on sleep bruxism (SB) in children and adolescents. It aims to assess the landscape of knowledge published in the last 20 years. METHODS: A total of 144 relevant publications from 386 previously identified through Medline were included, of which 83 were on possible SB, 37 on probable SB, 20 on definite SB and 4 were non-applicable. The review places emphasis on the recent evidence on prevalence, pathophysiology, diagnosis and management of SB in children and adolescents, with special focus on probable and definitive SB. RESULTS: The prevalence ranges from 5% to 50% depending on the age range and on the SB diagnosis (possible, probable or definitive). The pathophysiology is multifactorial, arousal associated and with behavioural problems and sleep disorders (obstructive sleep apnoea, snoring, nightmares) as risk factors, as well as respiratory conditions (allergies, oral breathing). Screening should include questionnaires and dental assessment. Instrumental recording is helpful to confirm diagnosis although more studies are needed to validate this approach in children. SB management includes controlling orofacial and dental consequences and assessing for any other comorbidity. Management options include occlusal splints, oral appliances (advancement mandibular), rapid maxillary expansion and some medications, although this last option is supported by limited evidences in children. CONCLUSION: Suggestions of future topics in research are delivered to better understand comorbidities, diagnosis and management with improved outcomes compared to what is currently available.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".