Validity of the Mattis Dementia Rating Scale to Detect Mild Cognitive Impairment in Parkinson’s Disease and REM Sleep Behavior Disorder
Bibliographic record
Abstract
BACKGROUND/AIMS: Mild cognitive impairment (MCI) is frequent in Parkinson's disease (PD) and idiopathic REM sleep behavior disorder (iRBD). However, only a few studies have evaluated the validity of brief cognitive measures to detect MCI in PD or iRBD using standard diagnostic criteria for MCI. Our aim was to evaluate the validity of the Mini-Mental State Examination (MMSE) and the Mattis Dementia Rating Scale (DRS-2) to detect MCI in PD and iRBD. METHODS: Forty PD patients and 34 iRBD patients were studied. Receiver operating characteristic curves were created for both tests to assess their effectiveness in identifying MCI in PD and iRBD. RESULTS: In PD, a normality cutoff of 138 on the DRS-2 yielded the best balance between sensitivity (72%) and specificity (86%) with a correct classification of 80%. In iRBD, the optimal normality cutoff was 141 on the DRS-2, with a sensitivity of 90%, a specificity of 71% and a correct classification of 82%. No cutoff for the MMSE was found to have acceptable sensitivity or specificity. CONCLUSION: The DRS-2 has satisfactory validity to detect MCI in PD or iRBD. The MMSE proved to be invalid as a screening test for MCI in both populations.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".