The application of Montreal Cognitive Assessment Chinese version in differentiation of Alzheimer's disease and vascular dementia
Bibliographic record
Abstract
Objective To assess the use of the Chinese version of Montreal Cognitive Assessment (MoCA) in differentiating Alzheimer's disease (AD) and vascular dementia (VD).Methods A total of 62 patients with AD and 54 patients with VD were recruited for this study.All subjects were subject to examination using MoCA to collect information in terms of their visuospatial/executive function,attention,language,abstraction delayed recall and orientation.The demographic data of the subjects was also were collected and analyzed.Results It was shown that there were statistically significant differences between the AD and VD patients with regard to their scores of visuospatial / executive,attention,delayed recall (P ＜ 0.05).The rate of diagnostic coincidence was 100％ in AD patients using MoCA Chinese version,and 98.15％ in VD patients,with a statistically significant difference between the two groups.There was high correlation in all items of MoCA between the two evaluators (ICC:0.911 ～1.000).Conclusion Montreal Cognitive Assessment Chinese version can be used for the diagnosis of AD and VD,and the scale can help differentiate AD and VD. Key words: The Montreal Cognitive Assessment; Alzheimer's disease; Vascular dementia
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".