Females with Sleep Bruxism Show Lower Theta and Alpha Electroencephalographic Activity Irrespective of Transient Morning Masticatory Muscle Pain
Bibliographic record
Abstract
AIMS: To investigate the hypothesis that the presence of transient morning masticatory muscle pain in young, healthy sleep bruxers (SBr) is associated with sex-related differences in sleep electroencephalographic (EEG) activity. METHODS: Data on morning masticatory muscle pain and sleep variables were obtained from visual analog scales and a second night of polysomnographic recordings. Nineteen normal control (CTRL) subjects were age- and sex-matched to 62 tooth-grinding SBr. Differences in sleep macrostructure (stage distribution and duration, number of sleep-stage shifts), number of rhythmic masticatory muscle activity (RMMA) events÷ hour, and EEG activity were analyzed blind to subject status. The influence of pain and gender in SBr and CTRL subjects was assessed with the Fisher's exact test, Mann-Whitney U test, two-sample t test, and analysis of variance (ANOVA). RESULTS: Low-intensity morning transient orofacial pain was reported by 71% of SBr, with no sex difference. RMMA event frequency was higher in SB than CTRL subjects (4.5÷hour vs 1.3÷hour; P < .001). SBr had fewer sleep-stage shifts, irrespective of sex or pain status. Female SBr had significantly lower theta and alpha EEG activity compared to female CTRL subjects (P = .03), irrespective of pain. CONCLUSION: Female SBr had lower theta and alpha EEG activity irrespective of transient morning pain.
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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 | 0.000 | 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) | 0.004 | 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".