Test the Best: Classification Accuracies of Four Cognitive Rating Scales for Parkinson’s Disease Mild Cognitive Impairment
Bibliographic record
Abstract
OBJECTIVE: A progressive cognitive impairment is one of the frequent non-motor symptoms during Parkinson's disease (PD) course. A short and valid screening tool is needed to detect an incipient cognitive deficit at the mild cognitive impairment stage in Parkinson's disease (PD-MCI). METHOD: The present study aims to evaluate the classification accuracies of four cognitive screenings: Montreal Cognitive Assessment (MoCA), Mattis Dementia Rating Scale second edition (DRS-2), Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB) in a cohort of PD patients (PD-MCI, n = 46; and Parkinson's disease with normal cognition, PD-NC, n = 95) and Controls (n = 66). All subjects underwent a standard neuropsychological battery as recommended by the International Parkinson and Movement Disorder Society and underwent all four screening tools. RESULTS: In the detection of PD-MCI versus PD-NC, the MoCA showed a sensitivity of 84% and a specificity of 66% with a screening cutoff score at ≤25 points. The MoCA's AUC was 86% (95% CI 78.7-93.1). In the detection of PD-MCI versus Controls, the FAB displayed 84% sensitivity and 79% specificity with a cutoff ≤16 points, to screen. The FAB's AUC was 87% (79.0-95.0). CONCLUSIONS: Our results show that the MoCA is the most discriminative tool for screening MCI in the PD population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".