Variability in sleep bruxism activity over time
Bibliographic record
Abstract
Sleep bruxism (SB) is an oral activity associated with jaw movements and tooth grinding. Sleep bruxism is believed to be highly variable over time, with subjects showing no activity on some nights and intense activity on others. Assessment of SB variability in individual patients is necessary for clinical trials designed to estimate the efficacy of SB management strategies. The present study analysed SB night-to-night variability over time in nine moderate to severe SB patients. Excluding the first night for habituation, a total of 37 nights were analysed, with a range of 2-8 nights per subject. The interval between the first and the last recording was between 2 months and 7.5 years. The outcomes were the number of SB episodes per hour, number of SB bursts per hour and number of SB episodes with grinding noise. The within subject variability of the three SB oromotor outcomes was evaluated using standard deviation (SD) and coefficient of variation. To verify the diagnosis of subjects over time, the values of the oromotor outcomes were compared with a standard research diagnostic cut-off: (1) Number of SB episodes per hour >4, (2) Number of SB bursts per hour >25, (3) Number of SB episodes with noise per night >1 (Lavigne et al. 1996). The mean coefficient of variation for the nine subjects was 25.3% for SB episodes per hour, 30.4% for SB bursts per hour and 53.5% for episodes with noise. Linear regression showed that the number of SB episodes per hour of stages 1 and 2 explains a large proportion of the variability. The SB diagnosis remained constant over time for every subject: 35 nights over 37 respected criteria 1 and 2, while grinding was present every night. These results indicate that while the SB diagnostic remains relatively constant over time in moderate to severe sleep bruxers, individual variability could be important in some SB patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".