Detecting the Cognitive Prodrome of Dementia in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: More than 75% of Parkinson's disease (PD) patients will develop dementia. Previous studies on the cognitive predictors of dementia in PD had some methodological limitations and the cognitive tests identified as good predictors vary greatly. OBJECTIVE: This prospective cohort study aims to identify the optimal cognitive predictors of dementia in PD using complementary statistical methods. METHODS: Eighty PD patients without dementia underwent polysomnographic recording, a neurological examination, and a complete neuropsychological assessment at baseline. They were then followed for a mean of 4.3 years. Baseline group comparisons and survival analyses were used to identify optimal cognitive predictors. Moreover, patients who developed dementia were pair-matched at baseline according to age, sex, and education to healthy controls (2 : 1), and receiver operating characteristic curves were calculated for cognitive tests. RESULTS: At follow-up, 23 patients (29%) developed dementia. PD patients who developed dementia had poorer baseline performance and a higher proportion of clinically impaired performance on several cognitive tests. Impaired baseline performance on the Block Design subtest was the best independent predictor of dementia (HR = 8). Moreover, the Trail Making Test part B (time) and Verbal Fluency (semantic) had the best psychometric properties (area under the curve >0.90) for identifying PD patients at risk of dementia. CONCLUSION: The present study identified three cognitive tests as the most accurate to detect individuals with PD at high risk of developing 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".