Bruxism physiology and pathology: an overview for clinicians*
Bibliographic record
Abstract
Awake bruxism is defined as the awareness of jaw clenching. Its prevalence is reported to be 20% among the adult population. Awake bruxism is mainly associated with nervous tic and reactions to stress. The physiology and pathology of awake bruxism is unknown, although stress and anxiety are considered to be risk factors. During sleep, awareness of tooth grinding (as noted by sleep partner or family members) is reported by 8% of the population. Sleep bruxism is a behaviour that was recently classified as a 'sleep-related movement disorder'. There is limited evidence to support the role of occlusal factors in the aetiology of sleep bruxism. Recent publications suggest that sleep bruxism is secondary to sleep-related micro-arousals (defined by a rise in autonomic cardiac and respiratory activity that tends to be repeated 8-14 times per hour of sleep). The putative roles of hereditary (genetic) factors and of upper airway resistance in the genesis of rhythmic masticatory muscle activity and of sleep bruxism are under investigation. Moreover, rhythmic masticatory muscle activity in sleep bruxism peaks in the minutes before rapid eye movement sleep, which suggests that some mechanism related to sleep stage transitions exerts an influence on the motor neurons that facilitate the onset of sleep bruxism. Finally, it remains to be clarified when bruxism, as a behaviour found in an otherwise healthy population, becomes a disorder, i.e. associated with consequences (e.g. tooth damage, pain and social/marital conflict) requires intervention by a clinician.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".