Predictors of Conversion to α-Synucleinopathy Diseases in Idiopathic Rapid Eye Movement Sleep Behavior Disorder
Bibliographic record
Abstract
BACKGROUND: Idiopathic rapid eye movement sleep behavior disorder (iRBD) often precedes the development of α-synucleinopathy diseases. OBJECTIVE: We aimed to assess the predictive value of clinical variables and biomarkers for the early development of α-synucleinopathy diseases in subjects with iRBD. METHODS: 56 patients with RBD Screening Questionnaire (RBDSQ) scores ≥5 at baseline and subsequent visit were enrolled as probable iRBD from the Parkinson's Progression Markers Initiative (PPMI) database. Baseline clinical data and biomarkers were analyzed. The endpoint was defined as disease progression to α-synucleinopathy diseases. Cox proportional hazard and Kaplan-Meier analyses were used to evaluate the predictive values of the indicators. RESULTS: During a mean follow-up duration of 5.1 years, 15 of 56 patients (26.8%) developed α-synucleinopathy diseases. Baseline clinical variables, including University of Pennsylvania Smell Identification Test (UPSIT, HR = 26.18, p = 0.004), 15-item Geriatric Depression Scale (GDS, HR = 14.26, p = 0.001), Montreal Cognitive Assessment (MoCA, HR = 3.56, p = 0.025), and Hopkins Verbal Learning Test Total recall (HVLT-TR, HR = 3.70, p = 0.014); genotype status of TMEM175 (HR = 3.74, p = 0.017), SCN3A (HR = 5.81, p = 0.022) and NUCKS1 (HR = 0.342, p = 0.049); ratio of phosphorylated tau to total tau (p-tau/t-tau, HR = 8.36, p = 0.001) in cerebrospinal fluid; and gray matter atrophy in inferior frontal gyrus (IFG, HR = 15.49, p = 0.001) were associated with phenoconversion to α-synucleinopathy diseases. A model combined the three independent variables (UPSIT, TMEM175 and gray matter atrophy in IFG) exhibited significantly improved predictive performance. CONCLUSION: For patients with iRBD, progression to α-synucleinopathy diseases can be predicted with good accuracy using a model combining clinical variables and biomarkers, which could form a basis for future disease prevention.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".