Comparative sensitivity of the MoCA and Mattis Dementia Rating Scale‐2 in Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Clinicians and researchers commonly use global cognitive assessments to screen for impairment. Currently there are no published studies directly comparing the sensitivity and specificity of the Montreal Cognitive Assessment and Mattis Dementia Rating Scale-2 in PD. The objective of this study was to identify the relative sensitivity and specificity of the Montreal Cognitive Assessment and Mattis Dementia Rating Scale-2 in PD. METHODS: The Montreal Cognitive Assessment and Mattis Dementia Rating Scale-2 were administered to training and validation cohorts. Cutoff scores were determined within the training cohort (n = 85) to optimize sensitivity and specificity for cognitive impairment and were applied to an independent validation cohort (n = 521). RESULTS: The Montreal Cognitive Assessment was consistently sensitive across training and validation cohorts (90.0% and 80.3%, respectively), whereas the Mattis Dementia Rating Scale-2 was not (87.5% and 60.3%, respectively). In individual domains, the Montreal Cognitive Assessment remained sensitive to memory and visuospatial impairments (91.9% and 87.8%, respectively), whereas the Mattis Dementia Rating Scale-2 was sensitive to executive impairments (86.2%). CONCLUSION: The Montreal Cognitive Assessment and Mattis Dementia Rating Scale-2 demonstrated individual strengths. Future work should focus on developing domain-specific cognitive screening tools for PD. © 2018 International Parkinson and Movement Disorder Society.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".