Comparison of Mini-Mental State Examination and Montreal Cognitive Assessment Ratings Across Levels of Parkinson’s Disease Severity
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment (CI) is common in Parkinson's disease (PD) and an important cause of disability. Screening facilitates early detection of CI and has implications for management. Preclinical disability is when patients have functional limitations but maintain independence through compensatory measures. OBJECTIVE: The objective of this study was to investigate the relationship between scores on the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) with levels of PD severity and disability. METHODS: PD patients (n = 2,234) in a large observational study were stratified by disease severity, based on Total Unified Parkinson's Disease Rating Scale (Total UPDRS) and Hoehn and Yahr (HY) stage. Using MMSE (n = 1,184) or MoCA (n = 1,050) and basic (ADL) and instrumental activities of daily living (IADL) scales for disability, linear regression analysis examined associations between cognitive status and disability. RESULTS: Cognition and disability were highly correlated, with the strongest correlation between IADL and MoCA. Only 16.0% of mean MMSE scores were below threshold for CI (28) and only in advanced PD (Total UPDRS 60+, HY≥3). MoCA scores fell below CI threshold (26) in 66.2% of the sample and earlier in disease (Total UPDRS 30+, HY≥2), corresponding with impairments in ADLs. CONCLUSION: In a large clinical dataset, a small fraction of MMSE scores fell below cutoff for CI, reinforcing that MMSE is an insensitive screening tool in PD. MoCA scores indicated CI earlier in disease and coincided with disability. This study shows that MoCA, but not MMSE is sensitive to the emergence of early cognitive impairment in PD and correlates with the concomitant onset of disability.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".