Quantifying the risk of neurodegenerative disease in idiopathic REM sleep behavior disorder
Bibliographic record
Abstract
OBJECTIVE: Idiopathic REM sleep behavior disorder (RBD) is a potential preclinical marker for the development of neurodegenerative diseases, particularly Parkinson disease (PD) and Lewy body dementia. However, the long-term risk of developing neurodegeneration in patients with idiopathic RBD has not been established. Obtaining an accurate picture of this risk is essential for counseling patients and for development of potential neuroprotective therapies. METHODS: We conducted a follow-up study of all patients seen at the sleep disorders laboratory at the Hôpital du Sacré Coeur with a diagnosis of idiopathic RBD. Diagnoses of parkinsonism and dementia were defined according to standard criteria. Survival curves were constructed to estimate the 5-, 10-, and 12-year risk of developing neurodegenerative disease. RESULTS: Of 113 patients, 93 (82%) met inclusion criteria. The mean age of participants was 65.4 years and 75 patients (80.4%) were men. Over the follow-up period, 26/93 patients developed a neurodegenerative disorder. A total of 14 patients developed PD, 7 developed Lewy body dementia, 4 developed dementia that met clinical criteria for AD, and 1 developed multiple system atrophy. The estimated 5-year risk of neurodegenerative disease was 17.7%, the 10-year risk was 40.6%, and the 12-year risk was 52.4%. CONCLUSIONS: Although we have found a slightly lower risk than other reports, the risk of developing neurodegenerative disease in idiopathic REM sleep behavior disorder is substantial, with the majority of patients developing Parkinson disease and Lewy body dementia.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".