525 A population-based study of phenoconversion to parkinsonism from REM sleep behavior: a Population-based Study in the CLSA
Bibliographic record
Abstract
Abstract Introduction To date, studies have estimated the phenoconversion rate from sleep clinics, using polysomnography proven RBD. However, no population-based estimates have been reported, testing to what degree possible RBD, screened by questionnaire is associated with increased risk of neurodegeneration. Methods We included those aged 45–85 years, living in one of 10 Canadian provinces in between 2012–2015 (at the baseline), recruited via three population-based sampling methods. Dream enactment behavior/possible RBD was screened using the RBD1Q single question-questionnaire. De-novo parkinsonism was defined as free of pre-existing diagnosis at the baseline with a ‘new’ diagnosis at the follow-up (205–2019). Relative risk (log-binomial regression), hazard ratio (Cox regression), incidence rate (Poisson regression) between the affected group and the symptom naïve group were assessed, adjusting for age and sex (and total years of education and language). Results Overall, 58 participants phenoconverted into parkinsonism and 53 into dementia at the follow-up (mean intervals=3.06±0.37 years). Participants with dream enactment behavior had 2.75 times higher risk to phenoconvert into parkinsonism than the symptom-free. Similarly, those with dream enactment behavior at the baseline possessed higher risk to screening positive of parkinsonism. No difference in time to phenoconversion was found between groups, The results remained robust after excluding non-RBD related symptoms, such as apnea and non-REM sleep parasomnia. Conclusion Compared to symptom-free, those with pRBD had higher risk to developing parkinsonism in near future. Support (if any):
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".