Bibliographic record
Abstract
Objectives：This study was tried to know usefulness of the Montreal Cognitive Assessment(MoCA) to detect mild cognitive impairment(MCI), to identify any differences according to items, and to disclose variables associated with MoCA. Methods：The MoCA-K(Korean Version of the Montreal Cognitive Assessment) and the MMSE-K(Korean Version of the Mini-Mental State Examination) were performed to normal controls(N=25), patients with MCI(N=27), and patients with dementia of the Alzheimer’s type(N=26). Results：1) At cut-off of 23/24, sensitivity of the MoCA-K to detect MCI was 70% and specificity was 92%. While sensitivity of the MMSE-K was 11%, and specificity was 96%. In general, the MoCA-K had higher sensitivity than the MMSE-K, while specificity was similar. 2) There were more differences in items of the MoCA than those of the MMSE, especially in attention and abstraction(p＜0.001, respectively). 3) Total scores of the MoCA-K had positive correlation with total scores of the MMSE-K(γ=0.879, p＜0.01) and education(γ=0.489, p＜0.01), and negative correlation with age(γ=-0.550, p＜0.01). Conclusion：The MoCA test seemed to be more useful than the MMSE to detect mild cognitive impairment. However, bias from age and educational level might affect the test results like MMSE.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".