Feasibility study of the Montreal cognitive assessment of Chinese version for the detection of vascular cognitive impairment-no dementia in Han population
Bibliographic record
Abstract
Objective To explore feasibility of the Chinese version of MoCA for the detection of vascular cognitive impairment-no dementia (VaCIND) and control in a cross-sectional study. Methods One hundred and three Chinese Han were assessed by the MoCA and MMSE. 64 met criteria for VaCIND and 39 were considered cognitively normal. Sensitivities and specificities were calculated using the recommended cut-off scores,and ROC curve analyses were performed to determine optimal sensitivity and specificity. Results No differences were found between groups on age,gender,education degrees. According to their MoCA scores,cognitive impairments including memory,visuospatial, executive function, attention, language, and orientation sub-scores in VaCIND ((0.44 ± 0.96), (2.13 ±1.40), (1.90 ±1.02), (4.61 ±1.41), (4.23 ±1.40), (5.38 ±1.15)) significantly decreased compared with that in controls((2.92 ± 1.42) ,(3.16 ± 1.08) ,(3.32 ± 1.07) ,(5. 87 ±0.41) ,(5.34 ±0.75), (5.79 ±0. 70)) (P<0. 05). The MMSE scale was insensitive to cognitive impairment as compared with MoCA scale. Using cut-off score of 24,the MoCA exhibited excellent sensitivity (0.923) and specificity (0.906). Conclusion MoCA is a more sensitive instrument than the MMSE for the detection of VaCIND and warrants further investigation regarding its applicability in large group and varying ethnic groups. Key words: Vascular cognitive impairment-no dementia; Montreal Cognitive Assessment; Screening
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".