Risk Factors for Phenoconversion in <scp>Rapid Eye Movement</scp> Sleep Behavior Disorder
Bibliographic record
Abstract
OBJECTIVE: This study was undertaken to follow up predictive factors for α-synuclein-related neurodegenerative diseases in a multicenter cohort of idiopathic/isolated rapid eye movement sleep behavior disorder (iRBD). METHODS: Patients with iRBD from 12 centers underwent a detailed assessment for potential environmental and lifestyle risk factors via a standardized questionnaire at baseline. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow-up. The cumulative incidence of parkinsonism or dementia was estimated with competing risk analysis. Cox regression analyses were used to evaluate the predictive value of environmental/lifestyle factors over a follow-up period of 11 years, adjusting for age, sex, and center. RESULTS: Of 319 patients who were free of parkinsonism or dementia, 281 provided follow-up information. After a mean follow-up of 5.8 years, 130 (46.3%) patients developed neurodegenerative disease. The overall phenoconversion rate was 24.2% after 3 years, 44.8% after 6 years, and 67.5% after 10 years. Patients with older age (adjusted hazard ratio [aHR] = 1.05) and nitrate derivative use (aHR = 2.18) were more likely to phenoconvert, whereas prior pesticide exposure (aHR = 0.21-0.64), rural living (aHR = 0.53), lipid-lowering medication use (aHR = 0.59), and respiratory medication use (aHR = 0.36) were associated with lower phenoconversion risk. Risk factors for those converting to primary dementia and parkinsonism were generally similar, with dementia-first converters having lower coffee intake and beta-blocker intake, and higher occurrence of family history of dementia. INTERPRETATION: Our findings elucidate the predictive values of environmental factors and comorbid conditions in identifying RBD patients at higher risk of phenoconversion. ANN NEUROL 2022;91:404-416.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".