Dementia risk prediction in early Parkinson's disease: Validation and genetic integration of the Montreal Parkinson risk of dementia scale (MoPaRDS)
Bibliographic record
Abstract
Background Prediction models for dementia in Parkinson disease (PD) are needed to better identify high-risk patients, but existing risk models often lack validation in early-stage PD, when prognosis is most challenging. Objective This study aims to validate the Montreal Parkinson Risk of Dementia Scale (MoPaRDS) in six population-based cohorts of newly diagnosed PD and to evaluate if incorporating genetic factors ( GBA1 and APOE-ε4 ) enhances its performance. Methods We calculated MoPaRDS scores for 1108 newly diagnosed PD patients, and MoPaRDS + GBA1 + APOE for the 941 patients with complete genetic data. We assessed the scores’ performance in predicting dementia diagnosed over 10 years using time-dependent receiver operating characteristic (ROC) curves. Results Of the 1108 patients (mean age 69.5 ± 10.0 years; 61.0% men), 350 (31.6%) developed dementia. The area under the time-dependent ROC curve (AUC) was 0.79 for MoPaRDS and 0.80 for MoPaRDS + GBA1 + APOE. Subdividing patients based on their MoPaRDS scores revealed annual observed risks of PDD of 39.4% (n = 8; high risk-), 11.4% (n = 176; intermediate risk-), and 5.0% (n = 942; low risk-group). With the suggested cutoff of ≥4, MoPaRDS had a sensitivity of 21.7% and specificity of 94.9%. Including the genetic items improved the sensitivity to 36.4% while maintaining comparable performance for specificity (91.5%). Conclusions MoPaRDS demonstrates high specificity but limited sensitivity in early PD, highlighting that a one-size-fits-all approach is inadequate for predicting dementia risk in PD across different disease stages. Integrating genetic items increases sensitivity and identifies more newly diagnosed patients at higher risk of dementia, and may be a useful approach to assist dementia risk assessment in early-stage PD.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".