Comparison of ambulatory and polysomnographic recording of jaw muscle activity during sleep in normal subjects
Bibliographic record
Abstract
Clinicians and investigators need a simple and reliable recording device to diagnose or monitor sleep bruxism (SB). The aim of this study was to compare recordings made with an ambulatory electromyographic telemetry recorder (TEL-EMG) with those made with standard sleep laboratory polysomnography with synchronised audio-visual recording (PSG-AV). Eight volunteer subjects without current history of tooth grinding spent one night in a sleep laboratory. Simultaneous bilateral masseter EMG recordings were made with a TEL-EMG and standard PSG. All types of oromotor activity and rhythmic masseter muscle activity (RMMA), typical of SB, were independently scored by two individuals. Correlation and intra-class coefficient (ICC) were estimated for scores on each system. The TEL-EMG was highly sensitive to detect RMMA (0·988), but with low positive predictive value (0·231) because of a high rate of oromotor activity detection (e.g. swallowing and scratching). Almost 72% of false-positive oromotor activity scored with the TEL-EMG occurred during the transient wake period of sleep. A non-significant correlation between recording systems was found (r = 0·49). Because of the high frequency of wake periods during sleep, ICC was low (0·47), and the removal of the influence of wake periods improved the detection reliability of the TEL-EMG (ICC = 0·88). The TEL-EMG is sensitive to detect RMMA in normal subjects. However, it obtained a high rate of false-positive detections because of the presence of frequent oromotor activities and transient wake periods of sleep. New algorithms are needed to improve the validity of TEL-EMG recordings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".