Prevalence of bruxism in children with episodic migraine - a case–control study with polysomnography
Bibliographic record
Abstract
BACKGROUND: Parents of children with migraine have described a higher prevalence of sleep bruxism and other sleep disturbances in their children. The objective of this study was to use polysomnography to investigate the prevalence of bruxism during sleep in children with episodic migraine relative to controls. FINDINGS: Controls and patients were matched by sex, age, years of formal education, presence of snoring, arousals per hour, and respiratory events per hour.A total of 20 controls, between 6 and 12 years old, with no history of headache, recruited from public schools in Sao Paulo between 2009 and 2012, and 20 patients with episodic migraine recruited from the Headache Clinic at the Federal University of Sao Paulo between 2009 and 2012 underwent polysomnography.No intervention was performed before sleep studies.Among migraine patients, 27.5% experienced aura prior to migraine onset. The sleep efficiency, sleep latency, REM sleep latency, arousals per hour, percentage of sleep stages, and breathing events per hour were similar between groups. Five children (25%) with episodic migraine exhibited bruxism during the sleep study while this finding was not observed in any control (p = 0.045). CONCLUSIONS: Our data demonstrate that bruxism during sleep is more prevalent in children with episodic migraine. Further prospective studies will help elucidate the underlying shared pathogenesis between bruxism and episodic migraine in children.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".