Sleep bruxism and sleep arousal: an experimental challenge to assess the role of cyclic alternating pattern
Bibliographic record
Abstract
Rhythmic masticatory muscle activity (RMMA) is the characteristic electromyographic pattern of sleep bruxism (SB), a sleep-related motor disorder associated with sleep arousal. Sleep arousals are generally organised in a clustered mode known as the cyclic alternating pattern (CAP). CAP is the expression of sleep instability between sleep maintaining processes (phase A1) and stronger arousal processes (phases A2 and A3). This study aimed to investigate the role of sleep instability on RMMA/SB occurrence by analysing CAP and electroencephalographic (EEG) activities. The analysis was performed on the sleep recordings of 8 SB subjects and 8 controls who received sensory stimulations during sleep. Baseline and experimental nights were compared for sleep variables, CAP, and EEG spectral analyses using repeated measure ANOVAs. Overall, no differences in sleep variables and EEG spectra were found between SB subjects and controls. However, SB subjects had higher sleep instability (more phase A3) than controls (P= 0·05). The frequency of phase A3 was higher in the pre-REM sleep periods (P < 0·001), where peaks in RMMA/SB activity were also observed (P = 0·05). When sleep instability was experimentally increased by sensory stimuli, both groups showed an enhancement in EEG theta and alpha power (P = 0·04 and 0·02, respectively) and significant increases in sleep arousal and all CAP variables. No change in RMMA/SB index was found within either groups (RMMA/SB occurred in all SB subjects and only one control during the experimental night). These findings suggest that CAP phase A3 may act as a permissive window rather than a generator of RMMA/SB activity in predisposed individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".