Sleep stage-dependent changes in tonic masseter and cortical activities in young subjects with primary sleep bruxism
Bibliographic record
Abstract
STUDY OBJECTIVES: The present study investigated the hypothesis that subjects with primary sleep bruxism (SB) exhibit masseter and cortical hyperactivities during quiet sleep periods that are associated with a high frequency of rhythmic masticatory muscle activity (RMMA). METHODS: Fifteen SB and ten control participants underwent polysomnographic recordings. The frequencies of oromotor events and arousals and the percentage of arousals with oromotor events were assessed. Masseter muscle tone during sleep was quantified using a cluster analysis. Electroencephalography power and heart rate variability were quantified and then compared between the two groups and among sleep stages. RESULTS: The frequency of RMMA and percentage of arousals with RMMA were significantly higher in SB subjects than in controls in all stages, while these variables for nonrhythmic oromotor events did not significantly differ between the groups. In SB subjects, the frequency of RMMA was the highest in stage N1 and the lowest in stages N3 and R, while the percentage of arousals with RMMA was higher in stage N3 than stages N1 and R. The cluster analysis classified masseter activity during sleep into two clusters for masseter tone and contractions. Masseter muscle tone showed typical stage-dependent changes in both groups but did not significantly differ between the groups. Furthermore, no significant differences were observed in electroencephalography power or heart rate variability between the groups. CONCLUSION: Young SB subjects exhibited sleep stage-dependent increases in the responsiveness of RMMA to transient arousals, but did not show masseter or cortical hyperactivity during sleep.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".