Reliability, Validity, and Optimal Cutoff Score of the Montreal Cognitive Assessment (Changsha Version) in Ischemic Cerebrovascular Disease Patients of Hunan Province, China
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".