Parkinson risk in idiopathic REM sleep behavior disorder
Bibliographic record
Abstract
OBJECTIVE: To precisely delineate clinical risk factors for conversion from idiopathic REM sleep behavior disorder (RBD) to Parkinson disease, dementia with Lewy bodies, and multiple system atrophy, in order to enable practical planning and stratification of neuroprotective trials against neurodegenerative synucleinopathy. METHODS: In a 10-year prospective cohort, we tested prodromal Parkinson disease markers in 89 patients with idiopathic RBD. With Kaplan-Meier analysis, we calculated risk of neurodegenerative synucleinopathy, and using Cox proportional hazards, tested the ability of prodromal markers to identify patients at higher disease risk. By combining predictive markers, we then designed stratification strategies to optimally select patients for definitive neuroprotective trials. RESULTS: The risk of defined neurodegenerative synucleinopathy was high: 30% developed disease at 3 years, rising to 66% at 7.5 years. Advanced age (hazard ratio [HR] = 1.07), olfactory loss (HR = 2.8), abnormal color vision (HR = 3.1), subtle motor dysfunction (HR = 3.9), and nonuse of antidepressants (HR = 3.5) identified higher risk of disease conversion. However, mild cognitive impairment (HR = 1.8), depression (HR = 0.63), Parkinson personality, treatment with clonazepam (HR = 1.3) or melatonin (HR = 0.55), autonomic markers, and sex (HR = 1.37) did not clearly predict clinical neurodegeneration. Stratification with prodromal markers increased risk of neurodegenerative disease conversion by 200%, and combining markers allowed sample size reduction in neuroprotective trials by >40%. With a moderately effective agent (HR = 0.5), trials with fewer than 80 subjects per group can demonstrate definitive reductions in neurodegenerative disease. CONCLUSIONS: Using stratification with simply assessed markers, it is now not only possible, but practical to include patients with RBD in neuroprotective trials against Parkinson disease, multiple system atrophy, and dementia with Lewy bodies.
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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 | 0.000 | 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) | 0.001 | 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".