Value of Montreal cognitive assessment in identifying patients with mild vascular cognitive impairment after first stroke
Bibliographic record
Abstract
Objective To determine the value of Montreal cognitive assessment (MoCA) in identifying the patients with mild vascular cognitive impairment after first stroke (mVCI-FS), and compare it's results with those of mini-mental state examination (MMSE). Methods MoCA and MMSE were performed on 60 patients with mVCI-FS and 25 with non mild vascular cognitive impairment after first stroke (nVCI-FS) by neurologists 12±1 w after the onset. Results Total mean scores of MoCA was 19.78±4.57 and that of MMSE was 25.48±3.14 with the partial correlation reaching r=0.779 and P=0.000. Significant differences in each sub-items of MoCA were found between mVCI-FS group and nVCI-FS group, except calculation and verbal fluency (P 0.05). The initial optimal cut-off-point of MoCA was 21 in identifying mVCI-FS from nVCI-FS according to the ROC curve analyses as well as the largest youden's index. With the cut-off-point of 21,MoCA Can provided a sensitivity of 84.6% and a specificity of 76.0%,respectively,for screening mVCI-FS, which was much better than MMSE (sensitivity 59.6% and specificity 57.7%)Conclusions The initial optimal cut-off-point of MoCA is 21 in identifying mVCI-FS from nVCI-FS.MoCA, having high sensitivity and specificity in screening mVCI-FS, is a valid screening scale in screening mVCI-FS; however, MMSE, showing poor sensitivity in screening mVCI-FS, cannot be a reliable instrument in screening mVCI-FS. Key words: Montreal cognitive assessment; Mini-mental state examination; Mild vascular cognitive impairment
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".