Rapid eye movement sleep behavior disorder and risk of dementia in Parkinson's disease: A prospective study
Bibliographic record
Abstract
One of the most devastating nonmotor manifestations of PD is dementia. There are few established predictors of dementia in PD. In numerous cross-sectional studies, patients with rapid eye movement (REM) sleep behavior disorder (RBD) have increased cognitive impairment on neuropsychological testing, but no prospective studies have assessed whether RBD can predict Parkinson's dementia. PD patients who were free of dementia were enrolled in a prospective follow-up of a previously published cross-sectional study. All patients had a polysomnogram at baseline. Over a mean 4-year follow-up, the incidence of dementia was assessed in those with or without RBD at baseline using regression analysis, adjusting for age, sex, disease duration, and follow-up duration. Of 61 eligible patients, 45 (74%) were assessed and 42 were included in a full analysis. Twenty-seven patients had baseline RBD, and 15 did not. Four years after the initial evaluation, 48% with RBD developed dementia, compared to 0% of those without (P-adjusted = 0.014). All 13 patients who developed dementia had mild cognitive impairment on baseline examination. Baseline REM sleep atonia loss predicted development of dementia (% tonic REM = 73.2 ± 26.7 with dementia, 40.8 ± 34.5 without; P = 0.029). RBD at baseline also predicted the new development of hallucinations and cognitive fluctuations. In this prospective study, RBD was associated with increased risk of dementia. This indicates that RBD may be a marker of a relatively diffuse, complex subtype of PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".