Understanding the clinical management of co‐occurring sleep‐related bruxism and obstructive sleep apnea in adults: A narrative and critical review
Bibliographic record
Abstract
Sleep-related bruxism (SRB) is a motor oral behavior characterized by tooth grinding and jaw clenching activity, reported by 8%-12% of the adult general population and 3% of older individuals. The frequency of one of its biomarkers, rhythmic masticatory muscle activity (RMMA), remains elevated across ages. Obstructive sleep apnea (OSA) is associated with the brief and repetitive pause of breathing (apnea) and with transient reduction in oxygen (hypoxia). OSA is observed at all ages and in about 50% of older individuals with a male preponderance. SRB clinical assessment is based on self-reporting of tooth grinding sound, awareness of clenching, jaw pain or headache, and clinical observation of tooth damage. OSA clinical assessment is based on sleepiness and fatigue, snoring, sleep quality, and awareness of breathing cessation, plus clinical examination of anatomical factors (e.g., obesity, retrognathia, large tonsil, macroglossia), age, gender, and body mass. Although the literature does not support association or causality between these two conditions, the co-occurrence is reported in about 30%-50% of adults. To confirm a diagnosis of co-occurring SRB and OSA, home sleep testing (HST) may be indicated. A sleep test is performed using electromyography (EMG) of jaw muscle (masseter or temporalis) and cardio-respiratory variables (e.g., air flow, respiratory effort, oxygen level, heart rate). The management of co-occurring SRB and OSA for individuals with prosthodontic needs is challenging to prevent compromising the oro-pharyngeal space and breathing efficiency. OSA treatment in the presence of SRB includes continuous positive airway pressure (CPAP) use alone or with an occlusal splint or mandibular advancement device (MAD). In addition, the following may be considered: supine sleep correction device, myofuncional therapy, medications, and surgeries. All have limitations and risks. Individual variability suggests that phenotyping is mandatory to select the most efficient and personalized treatment.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".