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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".