Validation of Neuropsychological Tests to Screen for Dementia in Chinese Patients With Parkinson’s Disease
Bibliographic record
Abstract
To compare the accuracy of different neuropsychological tests and their combinations for deriving reliable cognitive indices for dementia diagnosis in Parkinson's disease (PD). One hundred forty consecutive patients with PD were recruited and administrated an extensive battery of neuropsychological tests. Discriminant analysis and receiver-operator characteristic curve were used to evaluate their correct classifications and validity. Patients with PD having dementia (PDD; 23.5%) performed significantly worse in all tests than patients without dementia. Age of onset, disease duration, Hoehn-Yahr grade, Unified Parkinson's Disease Rating Scale part III scores, and education were associated with dementia in patients with PD. Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment, and Block Design (BD) showed better specificity and sensitivity when used alone, and combined use of MMSE and BD further increased the validity. Our results indicated that the accuracy of MMSE was better in dementia diagnosis of Chinese patients with PD, and combined use of MMSE and BD could further increase the validity of dementia diagnosis.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".