Oro‐facial activities in sleep bruxism patients and in normal subjects: a controlled polygraphic and audio–video study
Bibliographic record
Abstract
To our knowledge, the large spectrum of sleep motor activities (SMA) present in the head and neck region has not yet been systematically estimated in normal and sleep bruxism (SB) subjects. We hypothesized that in the absence of audio-video signal recordings, normal and SB subjects would present a high level of SMA that might confound the scoring specificity of SB. A retrospective analysis of several SMA, including oro-facial activities (OFA) and rhythmic masticatory muscle activities (RMMA), was made from polygraphic and audio-video recordings of 21 normal subjects and 25 SB patients. Sleep motor activities were scored, blind to subject status, from the second night of sleep recordings. Discrimination of OFA included the following types of activities: lip sucking, head movements, chewing-like movements, swallowing, head rubbing and scratching, eye opening and blinking. These were differentiated from RMMA and tooth grinding. The frequency of SMA per hour of sleep was lower in normal subjects in comparison with SB patients (P < 0.001). Up to 85% of all SMA in normal subjects were related to OFA while 30% of SMA in SB patients were related to OFA scoring (P < 0.001). The frequency of RMMA was seven times higher in SB patients than in normal subjects (P < 0.001). Several SMA can be observed in normal and SB subjects. In the absence of audio-video signal recordings, the discrimination of various types of OFA is difficult to achieve and may lead to erroneous estimation of SB-related activities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".