Sleep architecture as a candidate for phenotyping sleep bruxism: A narrative physiological review
Bibliographic record
Abstract
BACKGROUND: Sleep bruxism (SB), an oral behaviour in otherwise healthy individuals, is characterised by frequent rhythmic masticatory muscle activity (RMMA) during sleep. RMMA/SB episodes occur over various sleep stages (N1-N3 and rapid eye movement (REM)), sleep cycles (non-REM to REM), and frequently with microarousals. It currently remains unclear whether these characteristics of sleep architecture are phenotype candidates for the genesis of RMMA/SB. OBJECTIVES: This narrative review investigated the relationship between sleep architecture and the occurrence of RMMA as a SB phenotype candidate. METHODS: PubMed research was performed using keywords related to RMMA/SB and sleep architecture. RESULTS: In non-SB and SB healthy individuals, RMMA episodes were most frequent in the light non-REM sleep stages N1 and N2, particularly during the ascending phase of sleep cycles. The onset of RMMA/SB episodes in healthy individuals was preceded by a physiological arousal sequence of autonomic cardiovascular to cortical activation. It was not possible to extract a consistent sleep architecture pattern in the presence of sleep comorbidities. The lack of standardisation and variability between subject complexified the search for specific sleep architecture phenotype(s). CONCLUSION: In otherwise healthy individuals, the genesis of RMMA/SB episodes is largely affected by oscillations in the sleep stage and cycle as well as the occurrence of microarousal. Furthermore, a specific sleep architecture pattern cannot be confirmed in the presence of sleep comorbidity. Further studies are needed to delineate sleep architecture phenotype candidate(s) that contribute to the more accurate diagnosis of SB and treatment approaches using standardised and innovative methodologies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".