REM sleep behavior disorder predicts cognitive impairment in Parkinson disease without dementia
Bibliographic record
Abstract
OBJECTIVE: To assess the relationship between the presence of REM sleep behavior disorder (RBD) and the cognitive profile of nondemented patients with Parkinson disease (PD). BACKGROUND: Cognitive impairment is an important nonmotor symptom in PD. Waking EEG slowing in nondemented PD has been related to the presence of RBD, a parasomnia affecting brainstem structures and frequently reported in PD. For this reason, RBD may be associated with cognitive impairment in PD. METHODS: Thirty-four patients with PD (18 patients with polysomnographic-confirmed RBD and 16 patients without RBD) and 25 healthy control subjects matched for age and educational level underwent sleep laboratory recordings and a comprehensive neuropsychological assessment. RESULTS: Patients with PD and concomitant RBD showed significantly poorer performance on standardized tests measuring episodic verbal memory, executive functions, as well as visuospatial and visuoperceptual processing compared to both patients with PD without RBD and control subjects. Patients with PD without RBD had no detectable cognitive impairment compared to controls. CONCLUSIONS: This study shows that cognitive impairment in nondemented patients with Parkinson disease (PD) is closely related to the presence of REM sleep behavior disorder, a sleep disturbance that was not controlled for in previous studies assessing cognitive deficits in PD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".