REM sleep behavior disorder and REM sleep without atonia in Parkinson’s disease
Bibliographic record
Abstract
OBJECTIVE: To determine the frequency of REM sleep behavior disorder (RBD) among patients with PD using both history and polysomnography (PSG) recordings and to further study REM sleep muscle atonia in PD. BACKGROUND: The reported occurrence of RBD in PD varies from 15 to 47%. However, no study has estimated the frequency of RBD using PSG recordings or analyzed in detail the characteristics of REM sleep muscle atonia in a large group of unselected patients with PD. METHODS: Consecutive patients with PD (n = 33) and healthy control subjects (n = 16) were studied. Each subject underwent a structured clinical interview and PSG recording. REM sleep was scored using a method that allows the scoring of REM sleep without atonia. RESULTS: One third of patients with PD met the diagnostic criteria of RBD based on PSG recordings. Only one half of these cases would have been detected by history. Nineteen (58%) of 33 patients with PD but only 1 of 16 control subjects had REM sleep without atonia. Of these 19 patients with PD, 8 (42%) did not present with behavioral manifestations of RBD, and their cases may represent preclinical forms of RBD associated with PD. Moreover, the percentage of time spent with muscle atonia during REM sleep was lower among patients with PD than among healthy control subjects (60.1% vs 93.2%; p = 0.003). CONCLUSIONS: RBD and REM sleep without atonia are frequent in PD as shown by PSG 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".