Reliability, Validity, and Optimal Cutoff Score of the Montreal Cognitive Assessment (Changsha Version) in Ischemic Cerebrovascular Disease Patients of Hunan Province, China
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Bibliographic record
Abstract
BACKGROUND/AIMS: The goal of this study was to examine the reliability and validity of the Changsha version of the Montreal Cognitive Assessment (MoCA-CS) in ischemic cerebrovascular disease patients of Hunan Province, China, and to explore the optimal cutoff score for detecting vascular cognitive impairment-no dementia (VCI-ND) and vascular dementia (VD). METHODS: Three hundred and thirty-eight ischemic cerebrovascular disease patients (131 with normal cognition, 111 with VCI-ND, and 96 with VD) and 132 healthy controls were recruited. All participants accepted examination by the MoCA-CS, Mini-Mental State Examination (MMSE), and other related scales. A detailed neuropsychological battery was used for making a final cognitive diagnosis. SPSS 16.0 statistical software was used for reliability, validity examination, and optimal cutoff score detection. RESULTS: Cronbach's α of the MoCA-CS was 0.884, and test-retest and interrater reliability of the MoCA-CS were 0.966 and 0.926, respectively. MoCA-CS scores were highly correlated with MMSE scores (r = 0.867) and simplified intelligence quotients (r = 0.822). The results indicate that 1 point should be added for subjects with less than 6 years of education, and that the optimal cutoff score for detecting VCI-ND is 26/27 (sensitivity 96.1%, specificity 75.6%), whereas the optimal cutoff score for detecting VD is 16/17 (sensitivity 92.7%, specificity 96.3%). CONCLUSION: The MoCA-CS has good reliability and validity, and is a useful cognitive screening instrument for detecting VCI in the Chinese population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it