The MMSE and MoCA for Screening Cognitive Impairment in Less Educated Patients with Parkinson’s Disease
Bibliographic record
Abstract
OBJECTIVE: To explore whether the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) can be used to screen for dementia or mild cognitive impairment (MCI) in less educated patients with Parkinson's disease (PD). METHODS: We reviewed the medical records of PD patients who had taken the Korean MMSE (K-MMSE), Korean MoCA (K-MoCA), and comprehensive neuropsychological tests. Predictive values of the K-MMSE and K-MoCA for dementia or MCI were analyzed in groups divided by educational level. RESULTS: The discriminative powers of the K-MMSE and K-MoCA were excellent [area under the curve (AUC) 0.86-0.97] for detecting dementia but not for detecting MCI (AUC 0.64-0.85). The optimal screening cutoff values of both tests increased with educational level for dementia (K-MMSE < 15 for illiterate, < 20 for 0.5-3 years of education, < 23 for 4-6 years, < 25 for 7-9 years, and < 26 for 10 years or more; K-MoCA < 7 for illiterate, < 13 for 0.5-3 years, < 16 for 4-6 years, < 19 for 7-9 years, < 20 for 10 years or more) and MCI (K-MMSE < 19 for illiterate, < 26 for 0.5-3 years, < 27 for 4-6 years, < 28 for 7-9 years, and < 29 for 10 years or more; K-MoCA < 13 for illiterate, < 21 for 0.5-3 years, < 23 for 4-6 years, < 25 for 7-9 years, < 26 for 10 years or more). CONCLUSION: Both MMSE and MoCA can be used to screen for dementia in patients with PD, regardless of educational level; however, neither test is sufficient to discriminate MCI from normal cognition without additional information.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".