Clinical Validity of the Mattis Dementia Rating Scale-2 in Parkinson Disease With MCI and Dementia
Bibliographic record
Abstract
The utility of the Mattis Dementia Rating Scale 2 (MDRS-2) in screening for dementia in Parkinson disease (PD) is well documented. However, little is known about its sensitivity to mild cognitive impairment in PD (PD-MCI). This study sought to document the validity of the MDRS-2 for diagnoses of PD-MCI and dementia in PD (PDD). Twenty-two healthy controls (HCs), 22 PD-MCI, and 16 PDD were compared on each MDRS-2 subscales and MDRS-2 total standard scores. Patients with PDD performed significantly worse than the other groups (all Ps < .05) on the MDRS-2 total and on all subscales, except attention. PD-MCI had significant lower scores than HCs on the MDRS-2 total and on initiation/perseveration and memory subscales. The optimal cutoff score for PD-MCI diagnosis was ≤ 140/144 and ≤ 132/144 for PDD. These findings suggest that MDRS-2 is a useful tool to identify dementia but that there might be a ceiling effect in the MDRS-2 cutoff score to diagnose MCI in PD.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".