Sleep less and bite more: Sleep disorders associated with occlusal loads during sleep
Bibliographic record
Abstract
Occlusal overload during sleep is a significant clinical issue that has negative impacts on the maintenance of teeth and the longevity of dental prostheses. Sleep is usually viewed as an 'out-of-functional' mode for masticatory muscles. However, orodental structures and prostheses are not free from occlusal loads during sleep since masticatory muscles can be activated at a low level within normal sleep continuity. Thus, an increase in masticatory muscle contractions, by whatever the cause, can be associated with a risk of increased occlusal loads during sleep. Among such conditions, sleep bruxism (SB) is a type of sleep-related movement disorders with potential load challenge to the tooth and orofacial structures. Patients with SB usually report frequent tooth grinding noises during sleep and there is a consecutive increase in number and strength of rhythmic masticatory muscle activity (RMMA). Other types of masticatory muscle contractions can be non-specifically activated during sleep, such as brief contractions with tooth tapping, sleep talking, non-rhythmic contractions related to non-specific body movements, etc.; these occur more frequently in sleep disorders. Studies have shown that clinical signs and symptoms of SB can be found in patients with sleep disorders. In addition, sleep becomes compromised with aging process, and a prevalence of most sleep disorders is high in the elderly populations, in which prosthodontic rehabilitations are more required. Therefore, the recognition and understanding of the role of sleep disorders can provide a comprehensive vision for prosthodontic rehabilitations when prosthodontists manage complex orodental cases needing interdisciplinary collaborations between dentistry and sleep medicine.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".