Craniofacial Pain and Jaw-muscle Activity during Sleep
Bibliographic record
Abstract
This study compared the jaw-muscle electromyographic (EMG) activity during sleep in patients with craniofacial pain (n = 63) or no painful conditions (n = 52) and between patients with tension-type headache (TTH: n = 30) and healthy control individuals (n = 30). All participants used a portable single-channel EMG device (Medotech A/S) for four nights. There was no significant difference in EMG activity between craniofacial pain (24.5 ± 17.9 events/hr) and no painful conditions (19.7 ± 14.5), or between TTH (20.8 ± 15.0) and healthy control individuals (15.2 ± 11.6, p >.050). There were positive correlations between EMG activity and number of painful muscles (r = 0.188; p = 0.044), characteristic pain intensity (r = 0.187; p = 0.046), McGill Pain Questionnaire (r = 0.251; p = 0.008), and depression scores (r = 0.291; p = 0.002). Patients with painful conditions had significantly higher night-to-night variability compared with pain-free individuals (p < 0.050). This short-term observational study suggests that there are no major differences between patients with different craniofacial pain conditions and pain-free individuals in terms of jaw-muscle EMG activity recorded with a single-channel EMG device during sleep. However, some associations may exist between the level of EMG activity and various parameters of craniofacial pain. Longitudinal studies are warranted to further explore the relationship between sleep bruxism and craniofacial 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) | 0.001 | 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".