Screening for Impaired Cognitive Domains in a Large Parkinson's Disease Population and Its Application to the Diagnostic Procedure for Parkinson's Disease Dementia
Bibliographic record
Abstract
BACKGROUND: Dementia is a new focus of research on improved treatment for Parkinson's disease (PD). In 2007, a screening tool for PD dementia (PD-D) was developed by the Movement Disorder Society (Level I testing), which still requires verification by a large population study. METHODS: We conducted a cross-sectional and multicenter study including 13 institutions administering the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) to 304 PD patients (mean age: 70.6 ± 8.3 years; mean Hoehn and Yahr stage: 2.7 ± 0.7). RESULTS: In all, 34.5% of the patients had MMSE scores <26; 94.3% of these patients had impairments in ≥2 cognitive domains and met the criteria for probable PD-D by Level I testing. Executive dysfunction combined with attention and memory impairment was most common (51.4%). In the Level I subtests of executive function, the score for phonemic fluency declined by <50% in patients with high MoCA scores (24-30 points) and lacked specificity for PD-D. No patient had visuospatial impairment (measured by the pentagon copying subtest) alone, and the score for pentagon copying stayed at ≥70% even in patients with low MMSE scores (12-25 points), therefore lacking sensitivity for PD-D. CONCLUSIONS: Level I testing with administration of the MMSE and MoCA is a practical and efficient screening tool for PD-D. However, the phonemic fluency and pentagon copying tests should be replaced by more specific/sensitive ones when screening for PD-D.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".