Risk Factor Profile in Parkinson’s Disease Subtype with REM Sleep Behavior Disorder
Bibliographic record
Abstract
BACKGROUND: Numerous large-scale studies have found diverse risk factors for Parkinson's disease (PD), including caffeine non-use, non-smoking, head injury, pesticide exposure, and family history. These studies assessed risk factors for PD overall; however, PD is a heterogeneous condition. One of the strongest identifiers of prognosis and disease subtype is the co-occurrence of rapid eye movement sleep behavior disorder (RBD).In previous studies, idiopathic RBD was associated with a different risk factor profile from PD and dementia with Lewy bodies, suggesting that the PD-RBD subtype may also have a different risk factor profile. OBJECTIVE: To define risk factors for PD in patients with or without associated RBD. METHODS: In a questionnaire, we assessed risk factors for PD, including demographic, medical, environmental, and lifestyle variables of 189 PD patients with or without associated polysomnography-confirmed RBD. The risk profile of patients with vs. without RBD was assessed with logistic regression, adjusting for age, sex, and disease duration. RESULTS: PD-RBD patients were more likely to have been a welder (OR = 3.11 (1.05-9.223), and to have been regular smokers (OR = 1.96 (1.04-3.68)). There were no differences in use of caffeine or alcohol, other occupations, pesticide exposure, rural living, or well water use. Patients with RBD had a higher prevalence of the combined family history of both dementia and parkinsonism (13.3% vs. 5.5% , OR = 3.28 (1.07-10.0). CONCLUSION: The RBD-specific subtype of PD may also have a different risk factor profile.
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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) | 0.003 | 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".