VALUE OF THE MONTREAL COGNITIVE ASSESSMENT IN DETECTION OF COGNITIVE IMPAIRMENT IN PATIENTS WITH CEREBRAL SMALL VESSEL DISEASE
Bibliographic record
Abstract
Objective To study the validity of Montreal Cognitive Assessment(MoCA) in the detection of cognitive impairment in patients with cerebral small vessel disease(SVD). Methods This study consisted of 103 SVD patients,who were divided into cognitive dysfunction group and cognitive normal group.Cognitive appraisal was conducted using MoCA and MMSE.Results Of cognitive dysfunction group,the total scores of MoCA and MMSE were 18.08±3.16 and 25.53±2.91,respectively,the two scores being correlated(r=0.522,P0.05).Compared with the cognitive normal group,the scores of MoCA and MMSE in dysfunction group were lower,the differences of subitems and total scores between the two groups were significant,except that one item attention in MoCA(t=3.53-12.22,P0.05),and of MMSE,only total score,memory and recall were significant difference between the two groups(t=2.00-3.67,P0.05).The best cutoff value of MoCA was 22/23,with a sensitivity of91.9% and a specificity of 95.1% in the identification of cognitive dysfunction in patients with SVD according to the ROC curveanalysis. Conclusion MoCA provides higher sensitivity and specificity than MMSE in screening cognitive dysfunction in SVD patients,with its optimal cutoff value of 22/23.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".