Sleep Bruxism in Children: A Narrative Review
Bibliographic record
Abstract
Sleep bruxism, characterized by involuntary grinding or clenching of the teeth and/or by bracing or thrusting of the mandible during sleep, is common in children. Sleep bruxism occurs while the patient is asleep. As such, diagnosis can be difficult as the affected child is usually unaware of the tooth grinding sounds. This article aims to familiarize physicians with the diagnosis and management of sleep bruxism in children. A search was conducted in May 2023 in PubMed Clinical Queries using the key terms "Bruxism" OR "Teeth grinding" AND "sleep". The search strategy included all observational studies, clinical trials, and reviews published within the past 10 years. Only papers published in the English literature were included in this review. According to the International classification of sleep disorders, the minimum criteria for the diagnosis of sleep bruxism are (1) the presence of frequent or regular (at least three nights per week for at least three months) tooth grinding sounds during sleep and (2) at least one or more of the following (a) abnormal tooth wear; (b) transient morning jaw muscle fatigue or pain; (c) temporary headache; or (d) jaw locking on awaking. According to the International Consensus on the assessment of bruxism, "possible" sleep bruxism can be diagnosed based on self-report or report from family members of tooth-grinding sounds during sleep; "probable" sleep bruxism based on self-report or report from family members of tooth-grinding sounds during sleep plus clinical findings suggestive of bruxism (e.g., abnormal tooth wear, hypertrophy and/or tenderness of masseter muscles, or tongue/lip indentation); and "definite" sleep bruxism based on the history and clinical findings and confirmation by polysomnography, preferably combined with video and audio recording. Although polysomnography is the gold standard for the diagnosis of sleep bruxism, because of the high cost, lengthy time involvement, and the need for high levels of technical competence, polysomnography is not available for use in most clinical settings. On the other hand, since sleep bruxism occurs while the patient is asleep, diagnosis can be difficult as the affected child is usually unaware of the tooth grinding sounds. In clinical practice, the diagnosis of sleep bruxism is often based on the history (e.g., reports of grinding noises during sleep) and clinical findings (e.g., tooth wear, hypertrophy and/or tenderness of masseter muscles). In childhood, sleep-bruxism is typically self-limited and does not require specific treatment. Causative or triggering factors should be eliminated if possible. The importance of sleep hygiene cannot be over-emphasized. Bedtime should be relaxed and enjoyable. Mental stimulation and physical activity should be limited before going to bed. For adults with frequent and severe sleep bruxism who do not respond to the above measures, oral devices can be considered to protect teeth from further damage during bruxism episodes. As the orofacial structures are still developing in the pediatric age group, the benefits and risks of using oral devices should be taken into consideration. Pharmacotherapy is not a favorable option and is rarely used in children. Current evidence on the effective interventions for the management of sleep bruxism in children is inconclusive. There is insufficient evidence to make recommendations for specific treatment at this time.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".