Vascular Cognitive Impairment: Most Useful Subtests of the Montreal Cognitive Assessment in Minor Stroke and Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND/AIMS: Cognitive impairment is frequent in cerebrovascular disease but often remains undetected. The Montreal Cognitive Assessment (MoCA) has been proposed in this context. Our aim was to evaluate the MoCA and its subtests in cerebrovascular disease. METHODS: We assessed 386 consecutive patients with minor stroke (National Institutes of Health Stroke Score <4) or transient ischemic attack at 3 months. The MoCA and the modified Rankin Scale (mRS) were administered. Computed tomography (CT) scans were assessed for stroke and white matter changes. An unfavorable functional outcome was defined as mRS >1. RESULTS: The prevalence of cognitive impairment (cutoff of 26) was 55% using the MoCA and 13% using the MMSE. In a multivariate analysis, MoCA <26 was associated with the outcome (OR 3.00, CI 1.78-5.03), as were remote lacunar stroke on CT and white matter changes of at least moderate severity. Five subtests (5-word recall, word list generation, trail-making, abstract reasoning and cube copy) formed an optimal short MoCA with 6/10 or less showing a sensitivity of 91% and a specificity of 83%. CONCLUSION: This study extends the utility of the MoCA to milder forms of cerebrovascular disease. The MoCA is associated with the 3-month functional outcome. Five subtests may constitute an optimal brief tool in vascular cognitive impairment.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".