The study on reliability,validity of Montreal Cognitive Assessment(Changsha Version) and preliminary exploration of its optimal cutoff score for detecting vascular cognitive impairment
Bibliographic record
Abstract
Objective To examine the reliability,validity of Montreal Cognitive Assessment(Changsha Version)(MoCA-CS) in patients with ischemic cerebrovascular disease in Hunan province and explore its optimal cutoff score for detecting vascular cognitive impairment(VCI).Methods MoCA-CS,Mini-Mental State Examination(MMSE),a detailed neuropsychological battery(including 4 sub-tests of Chinese revised Wechsler Adult Intelligence Scale: block design,digit span,similarities,and arithmetic;3 sub-tests of Chinese revised Wechsler memory scale: logical memory test and 5 min delayed logical memory,experience and orientation,visual recognition;stroop tests) and several related scales were conducted in 159 patients with ischemic cerebrovascular disease(all ≥40 years old) to evaluate their cognitive function.MoCA-CS was then re-tested in a randomly selected sub-sample(contained 30 people) 3-5 weeks after the first time cognitive estimation.The internal consistency reliability,test-retest reliability,inter-rater reliability,concurrent validity of MoCA-CS were calculated.Based on receiver operator characteristic curve(ROC) analysis,we explored MoCA-CS's optimal cutoff score for detecting VCI.Results The Cronbach's α of MoCA-CS was 0.846.The test-retest reliability and inter-rater reliability of MoCA-CS were 0.974 and 0.969,respectively.The concurrent validity of MoCA-CS was estimated by correlating its final scores with scores of MMSE and simplified intelligence quotient(S-IQ),both showed high correlation(r = 0.879 for MMSE and r = 0.799 for S-IQ).It was recommended adding 1 point to total scores of MoCA-CS for subjects with ≤6 years of education.26/27(sensitivity 90.0%,specificity 70.9%) was recommended as optimal cutoff score for detection VCI(≤26 indicates VCI).The consistency between diagnose results of clinical experts and MoCA-CS was good(Kappa = 0.610).Conclusions MoCA-CS has good reliability and validity,and is suitable for screening VCI in patients with ischemic cerebrovascular disease in Hunan province.MoCA-CS has the potential to further apply in the whole mainland China.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".