Sleep bruxism is associated to micro‐arousals and an increase in cardiac sympathetic activity
Bibliographic record
Abstract
Sleep bruxism (SB) subjects show a higher incidence of rhythmic masticatory muscle activity (RMMA) than control subjects. RMMA is associated with sleep micro-arousals. This study aims to: (i) assess RMMA/SB episodes in relation to sleep cycles; (ii) establish if RMMA/SB and micro-arousals occur in relation to the slow wave activity (SWA) dynamics; (iii) analyze the association between RMMA/SB and autonomic cardiac activity across sleep cycles. Two nights of polygraphic recordings were made in three study groups (20 subjects each): moderate to high SB, low SB and control. RMMA episodes were considered to occur in clusters when several groups of RMMA or non-specific oromotor episodes were separated by less than 100 s. Correlations between sleep, RMMA/SB index and heart rate variability variables were assessed for the first four sleep cycles of each study group. Statistical analyses were done with SYSTAT and SPSS. It was observed that 75.8% of all RMMA/SB episodes occurred in clusters. Micro-arousal and SB indexes were highest during sleep cycles 2 and 3 (P < 0.001). Within each cycle, micro-arousal and RMMA/SB indexes showed an increase before each REM sleep (P <or= 0.02). The cross-correlation plot for micro-arousal index showed positive association from 4 min preceding SB onset in the moderate to high SB subjects (P <or= 0.06). The cross-correlation plot revealed that SWA decreases following SB onset (P <or= 0.05). Further cross-correlation analysis revealed that a shift in sympatho-vagal balance towards increased sympathetic activity started 8 min preceding SB onset (P
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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".