Screening for Cognitive Impairment in a Stroke Prevention Clinic Using the MoCA
Bibliographic record
Abstract
BACKGROUND: Screening for cognitive impairment is recommended in patients with cerebrovascular disease. We sought to establish the incidence of cognitive impairment using the Montreal Cognitive Assessment (MoCA) in a cohort of consecutive patients attending our stroke prevention clinic (SPC), and to determine whether a subset of the MoCA could be derived for use in this busy clinical setting. METHODS: The MoCA was administered to 102 patients. Incidence of cognitive impairment was compared to presenting complaint and final diagnosis. extent of cerebral white matter changes (WMC) was rated using the Age Related White Matter Changes (ARWMC) scale in 80 patients who underwent neuroimaging. A subset of the three most predictive test elements of the MoCA was derived using regression analysis. RESULTS: 63.7% of patients scored <26/30 on the MoCA, in keeping with cognitive impairment. This was unrelated to the final diagnosis or extent of WMC, although a trend for lower MoCA scores was observed in older patients. A mini-MoCA subscore combining the clock drawing test, five-word delayed recall, and abstraction was highly correlated with the final MoCA score (R=0.901). A score of <7/10 using this 10-point mini-MoCA identified cognitive impairment as defined by the MoCA with a sensitivity of 98.5%, and a specificity of 77.6%. CONCLUSIONS: Two-thirds of SPC patients demonstrated evidence for cognitive impairment, irrespective of their final diagnosis or the presence of WMC. A mini-MoCA comprised of the clock drawing test, five-word delayed recall, and abstraction represents a potential alternative to the full MoCA in this population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".