Value of the Montreal cognitive assessment for the detection of vascular cognitive impairment in cerebral small vessel disease
Bibliographic record
Abstract
Objective Cognitive impairment that is caused by or associated with vascular factors has been termedvascular cognitive impairment,which comprises vascular dementia(VD) and vascular cognitive impairment no-dementia(VCIND).VCIND is a term that broadly encompasses cognitive deficits associated with vascular disease which fall short of a dementia diagnosis.In this study we aimed to evaluate the validity of Montreal cognitive assessment in cognitive impairment caused by cerebral small vessel disease(SVD).Methods According to the diagnostic criteria,103 patients with SVD were divided into two groups,cognitive impairment group(n=62) and the control(n=41).All the patients were assessed with MoCA and MMSE.Results(1)No significant differences were found between the two groups on age,gender and education level(P0.05).(2)The total scores of MoCA and MMSE were 18.20±3.42,25.53±2.91,respectively in the cognitive impairment group.There was high correlation between the total scores of MoCA and MMSE by using spearman correlation coefficient(r=0.531,P=0).(3)Total scores of MoCA and MMSE in the cognitive impairment group were significantly lower compared with that in control group.Except attention,significant differences in other sub-items of MoCA were found between the two groups(P0.05),however only total score,memory and recall had differences between two groups by MMSE.(4) According to the ROC curve analyses,with the best cut-off score of 22/23,MoCA can provide a sensitivity of 91.9% and a specificity of 95.1%.Conclusions MoCA has higher sensitivity and specificity than MMSE in screening cognitive impairment caused by SVD,and the optimal cut-off point of MoCA is 22/23.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".