Prediction of cognitive progression in Parkinson's disease using three cognitive screening measures
Bibliographic record
Abstract
INTRODUCTION: Cognitive impairment is a common complication of Parkinson's disease (PD) and identifying risk factors for progression to Parkinson's disease dementia (PDD) is important. However, little research has been done comparing the utility of commonly used cognitive screening tests in predicting cognitive progression in PD. METHODS: We retrospectively reviewed data from patients with PD enrolled in the Pacific Udall Center who had baseline and longitudinal neuropsychological and global cognitive screening tests. The diagnostic accuracies of 3 common screening tests were compared: Montreal Cognitive Assessment (MoCA), Mattis Dementia Rating Scale (DRS-2), and Mini Mental Status Examination (MMSE). Cognitive diagnoses of PD with mild cognitive impairment (PD-MCI) and PDD were based on full neuropsychological testing and established Movement Disorder Society criteria. Logistic regression and Cox proportional hazards regression models were used to examine predictors of cognitive decline. RESULTS: Four hundred seventy patients for whom scores on all 3 screening tests were available from the same assessment were included in a cross-sectional analysis. The MoCA demonstrated the best overall diagnostic accuracy for PD-MCI (AUC= 0.79, sensitivity= 76.4%) and for PDD (AUC= 0.89, sensitivity= 81.0%) compared to the DRS-2 and MMSE. A longitudinal analysis was performed on the subset of patients (316/470; 67.2%) who were nondemented at baseline and had undergone two or more assessments. After controlling for covariates, the MoCA was the only test associated with progression to PDD (OR= 1.27 95% CI 1.1 - 1.5, p=0.001) and faster time to dementia (HR = 1.3, 95% CI 1.1 - 1.4, p<0.0001). CONCLUSIONS: This study provides additional support for the use of the MoCA as a primary screening tool for cognitive impairment in PD and is the first to show that the MoCA is a predictor of conversion to PDD.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".