Comparison Between Automatic and Visual Scorings of REM Sleep Without Atonia for the Diagnosis of REM Sleep Behavior Disorder in Parkinson Disease
Bibliographic record
Abstract
Study Objectives: To compare three different methods, two visual and one automatic, for the quantification of rapid eye movement (REM) sleep without atonia (RSWA) in the diagnosis of REM sleep behavior disorder (RBD) in Parkinson's disease (PD) patients. Methods: Sixty-two consecutive patients with idiopathic PD underwent video-polysomnographic recording and showed more than 5 minutes of REM sleep. The electromyogram during REM sleep was analyzed by means of two visual methods (Montréal and SINBAR) and one automatic analysis (REM Atonia Index or RAI). RBD was diagnosed according to standard criteria and a series of diagnostic accuracy measures were calculated for each method, as well as the agreement between them. Results: RBD was diagnosed in 59.7% of patients. The accuracy (85.5%), receiver operating characteristic (ROC) area (0.833) and Cohen's K coefficient (0.688) obtained with RAI were similar to those of the visual parameters. Visual tonic parameters, alone or in combination with phasic activity, showed high values of accuracy (93.5-95.2%), ROC area (0.92-0.94), and Cohen's K (0.862-0.933). Similarly, the agreement between the two visual methods was very high, and the agreement between each visual methods and RAI was substantial. Visual phasic measures alone performed worse than all the other measures. Conclusion: The diagnostic accuracy of RSWA obtained with both visual and automatic methods was high and there was a general agreement between methods. RAI may be used as the first line method to detect RSWA in the diagnosis of RBD in PD, together with the visual inspection of video-recorded behaviors, while the visual analysis of RSWA might be used in doubtful cases.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".