Bibliographic record
Abstract
Purpose : This study aimed to present normative data and cut-off points for older Korean adults completing the Montreal Cognitive Assessment - Korean (MoCA-K), which is used as a screening test for mild cognitive impairment in Korea. Methods : A total of 195 healthy adults ≥60 years were recruited. All participants completed the MoCA-K and the Korean - Mini-Mental State Examination (MMSE-K) to assess their cognitive function. Participants were divided into six groups based on their age: 60-64 years, 65~69 years, 70~74 years, 75~79 years, 80~84 years, and 85~89 years. Results : The results revealed that MoCA-K score decreased significantly with age (mean score ± standard deviation [SD]; 27.63±2.80 in subjects aged 60~64 years; 27.00±2.39 in subjects aged 65~69 years; 24.94±2.96 in subjects aged 70~74 years; 24.74±3.37 in subjects aged 75~79 years; 22.59±4.72 in subjects aged 80~84 years; and 18.83±5.38 in subjects aged 85~89 years; p<.001). Additionally, MoCA-K score also increased significantly with educational level (mean score±standard deviation [SD]; 19.95±4.78 in no formal education group; 24.95±2.22 in elementary school graduated group; 26.35±2.72 in middle school graduated group; 28.32±1.36 in high school graduated group; and 28.50±1.51 in more than college graduated group; p<.001). The optimal cut-off points were 24/25 for 60~69 years old group, 21/22 for 70~79 years old group, 17/18 for 80~84 years old group, and 13/14 for 85~89 years old group. The optimal cut-off points were 15/16 for individuals who were illiterate, 22/23 for individuals with 6 years of education, 22/24 for individuals with 9 years of education, and 26/27 for individuals with 12 or more years of education. Conclusions : This study presents normative data and cut-off points for the MoCA-K in older Korean adults. This data will facilitate more accurate detection and follow-up of the risk of mild cognitive impairment in this population, taking into consideration age and education. Future studies are required that should focus on the cut-off score on the level of education according to age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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; both teacher heads agree on what is shown here.
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".