Network analysis of sleep bruxism in the <scp>EPISONO</scp> adult general population
Bibliographic record
Abstract
Sleep bruxism (SB) has been associated with biological and psychosocial factors. The assessment of SB includes self-report, clinical evaluation, and polysomnography. This study aimed to investigate the associations of self-reported SB with other sleep disorders and demographic, psychological, and lifestyle factors in the adult general population, and to investigate whether self-reported SB and polysomnographically (PSG) confirmed SB provide similar outcomes in terms of their associated factors. We recruited 915 adults from the general population in Sao Paulo, Brazil. All participants underwent a one-night PSG recording and answered questions about sex, age, BMI, insomnia, OSA risk, anxiety, depression, average caffeine consumption, smoking frequency, and alcohol consumption frequency. We investigated the link between SB and the other variables in univariate, multivariate, and network models, and we repeated each model once with self-reported SB and once with PSG-confirmed SB. Self-reported SB was only significantly associated with sex (p = 0.042), anxiety (p = 0.002), and depression (p = 0.03) in the univariate analysis, and was associated with insomnia in the univariate (p < 0.001) and multivariate (β = 1.054, 95%CI 1.018-1.092, p = 0.003) analyses. Network analysis showed that self-reported SB had a direct positive edge to insomnia, while PSG-confirmed SB was not significantly associated with any of the other variables. Thus, sleep bruxism was positively associated with insomnia only when self-reported, while PSG-confirmed SB was not associated with any of the included factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".