Comparison of ambulatory and polysomnographic recording of jaw muscle activity during sleep in normal subjects
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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) | 0.000 | 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 it