Polysomnographic diagnosis of idiopathic REM sleep behavior disorder
Bibliographic record
Abstract
The presence of either excessive tonic chin EMG activity during REM sleep, or excessive phasic submental or limb EMG twitching is required to diagnose REM sleep behavior disorder (RBD). The aim was to identify cut-off values and to assess the sensitivity and specificity of these values taken separately or combined to diagnose idiopathic RBD patients. Eighty patients presenting with a clinical diagnosis of idiopathic RBD and 80 age- and gender-matched normal controls were studied in the sleep laboratory. Receiver operating characteristic curves were drawn to find optimal cut-off values for three REM sleep EMG parameters. Tonic and phasic EMG activity were measured in the chin, but not in the limbs. Videos were examined during the recording but were not systematically reviewed by the authors. Total correct classification of 81.9% was found for tonic chin EMG density ≥30%; 83.8% for phasic chin EMG density ≥15% and 75.6% for ≥24 leg movements per hour of REM sleep. Five patients did not fulfill any of these three polysomnographic (PSG) criteria. Conversely, one subject of the control group met the PSG criteria for RBD. This study estimates the diagnostic value of a visual scoring method for the diagnosis of idiopathic RBD and establishes cut-off values to be used in clinical and research set-ups. For the five RBD patients who did not show chin EMG abnormalities, it cannot be excluded that they had increased phasic EMG activity in the upper limbs and presented visible motor activity.
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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 | 0.002 |
| 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) | 0.002 | 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".