Validation of the Conversion between the Mini-Mental State Examination and Montreal Cognitive assessment in Korean Patients with Parkinson’s Disease
Bibliographic record
Abstract
OBJECTIVE: Two conversion tables between the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) have recently been established for Parkinson's disease (PD). This study aimed to validate them in Korean patients with PD and to evaluate whether they could be influenced by educational level. METHODS: A total of 391 patients with PD who undertook both the Korean MMSE and the Korean MoCA during the same session were retrospectively assessed. The mean, median, and root mean squared error (RMSE) of the difference between the true and converted MMSE scores and the intraclass correlation coefficient (ICC) were calculated according to educational level (6 or fewer years, 7-12 years, or 13 or more years). RESULTS: Both conversions had a median value of 0, with a small mean and RMSE of differences, and a high correlation between the true and converted MMSE scores. In the classification according to educational level, all groups had roughly similar values of the median, mean, RMSE, and ICC both within and between the conversions. CONCLUSION: Our findings suggest that both MMSE-MoCA conversion tables are useful instruments for transforming MoCA scores into converted MMSE scores in Korean patients with PD, regardless of educational level. These will greatly enhance the utility of the existing cognitive data from the Korean PD population in clinical and research settings.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".