Comparison of Montreal Cognitive Assessment in Korean Version for Predicting Mild Cognitive Assessment in 65-Year and Over Individuals
Bibliographic record
Abstract
Objectives. The purpose of this study was to compare the validity and reliability of the two Korean versions of the MoCA for individuals aged ≥65 years. Methods. A total of 185 participants aged ≥65 years were included in this cross-sectional study. This study investigated the reliability of the two Korean versions of the MoCA (the MoCA-K and MoCA-K2) by having each participant complete both assessments twice and comparing them to their Korean version of the Mini-Mental State Exam (MMSE-K) scores. The participants either completed the tests in order A (MoCA-K2 before MoCA-K) then B (MoCA-K before MoCA-K2) or vice versa. The tests were then completed in the opposite order. This study conducted all experiments at 3-day intervals. Results. Of the 185 total participants analyzed, 95 indicated cognitive impairment, while 90 had normal in MoCA-K scores; 50 demonstrated cognitive impairment, while 135 had normal in MMSE-K scores; and 101 and 84 participants showed cognitive impairment and normal in MoCA-K2 scores, respectively. Cronbach’s α values were 0.929 for the MoCA-K, 0.774 for the MMSE-K, and 0.919 for the MoCA-K2. The mean scores were 22.37, 25.29, and 21.96 points for the MoCA-K, MMSE-K, and MoCA-K2, respectively. The sensitivity and the specificity of the MoCA-K were 77.0% and 78.0%, respectively, while those of the MoCA-K2 were 68.9% and 80.0%, respectively. Conclusions. These results suggest that both the MoCA-K and MoCA-K2 are suitable and reliable evaluation tools for MCI screening; however, the MoCA-K had better overall sensitivity and specificity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".