The utility of Beijing Version Montreal Cognitive Assessment in ischemic cerebrovascular disease patients of Changsha area and the development of Changsha Version Montreal Cognitive Assessment
Bibliographic record
Abstract
Objective To validate Beijing version Montreal Cognitive Assessment(MoCA) and develop Changsha version MoCA through further modifications of MoCA.Methods MoCA(Beijing Version) and Mini-Mental State Examination(MMSE) were employed to evaluate their cognition,daily life,mood,and psychiatric situation in 56 patients with ischemic cerebrovascular disease and 32 normal controls in Changsha area(all ≥40 years old) Regression and receiver operator characteristic curve(ROC curve) analyses were used to analyze every sub-item of Beijing version MoCA to validate and explore its potential modifications of Beijing version MoCA.Results The correlation between MoCA(Beijing Version) and MMSE was high(r=0.926).The areas under the receiver operator characteristic curve(ROC curve) for the cognitive impairment group versus normal group by MoCA(Beijing Version) were 0.907(95% confidence interval,0.848-0.966).When cutoff score was 25/26,its sensitivity and specificity for distinguishing cognitive impairment were 95.35% and 55.56%,respectively.When cutoff score was 23/24,its sensitivity and specificity for distinguishing cognitive impairment were 86.04% and 82.22%,respectively.3 disputed sub-items had entered small sample trial.After repeated discussion and modification,the final version of Changsha MoCA was developed in July,2010.Conclusions Beijng version MoCA is an effective and feasible cognitive screening scale.However,it still has several insufficiencies which restrict its utility in population of mainland China.In contrast,Changsha version MoCA is a cognitive screen scale especially for population of mainland China.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".