Analysis of the sleep period and the amount of habitual snoring in individuals with sleep bruxism
Bibliographic record
Abstract
BACKGROUND: The literature does not report any association between habitual snoring and sleep bruxism, but these situations can be a reason for frequent complaints of individuals, impairing the quality of life. This study was performed to investigate the sleep period and amount of habitual snoring in individuals with sleep bruxism observing expiratory, inspiratory, and mixed snoring. MATERIAL AND METHODS: A total of 90 individuals were screened and divided into the following groups: with sleep bruxism (n=45) and those without sleep bruxism (n=45). Single night sleep polysomnography was performed to diagnose sleep bruxism, quantify habitual snoring and sleep period. The results were tabulated and submitted to a Multivariate analysis of variance (MANOVA) to compare the means of the two independent groups, considering the affected diagnosis of sleep bruxism, snoring (independent variables) and age as covariate. For the post hoc, was used correcting for multiple comparisons (Bonferroni test, P<.05). RESULTS: There was statistically significant difference among the groups ( p=.001) in the sleep period, in that individuals with sleep bruxism slept for a longer duration (with sleep bruxism group: 460 minutes and without sleep bruxism group: 401 minutes). There were no statistically significant differences among the groups for the number of inspiratory, expiratory and mixed snores, but was observed greater amount of snoring in the with sleep bruxism group. CONCLUSIONS: The main finding of this study is that individuals with sleep bruxism slept longer than the control group. It may also be suggested that individuals with sleep bruxism tended to increase the amount of habitual snoring during sleep.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".