Level I PD-MCI Using Global Cognitive Tests and the Risk for Parkinson's Disease Dementia.
Bibliographic record
Abstract
BACKGROUND: The criteria for PD-MCI allow the use of global cognitive tests. Their predictive value for conversion from PD-MCI to PDD, especially compared to comprehensive neuropsychological assessment, is unknown. METHODS: The MDS PD-MCI Study Group combined four datasets containing global cognitive tests as well as a comprehensive neuropsychological assessment to define PD-MCI (n = 467). Risk for developing PDD was examined using a Cox model. Global cognitive tests were compared to neuropsychological test batteries (Level I&II) in determining risk for PDD. RESULTS: PD-MCI based on a global cognitive test (MMSE or MoCA) increases the hazard for developing PDD (respectively HR = 2.57, P = 0.001; HR = 4.14, P = <0.001). The C-statistics for MMSE (0.72) and MoCA (0.70) were lower than those based on neuropsychological tests (Level I = 0.82; Level II = 0.81). Sensitivity, specificity and diagnostic accuracy balance was best in Level II. CONCLUSION: MMSE and MoCA predict conversion to PDD. However, Level II neuropsychological assessment seems the preferred assessment for PD-MCI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".