Performances on the Montreal Cognitive Assessment Along the Cardiovascular Disease Continuum
Bibliographic record
Abstract
Cardiovascular diseases involve a continuum starting with risk factors, which can progress to coronary heart disease and eventually, to heart failure. Cognitive impairment (CI) is observed as early as cardiovascular risk factors, and in up to 50% of patients with heart failure. Because CI in cardiovascular disease is linked to poorer clinical outcomes, early detection is essential. The Montreal Cognitive Assessment (MoCA) is a screening tool widely used in clinical setting. To date, little is known about MoCA scores along the cardiovascular disease continuum. OBJECTIVE: This study compared performances of different cardiovascular disease profiles on the MoCA and its subscores. METHOD: Eighty participants (>50 years) from two studies conducted at the Montreal Heart Institute were separated into four groups: low cardiovascular risk factors (<2), high cardiovascular risk factors (>2), coronary heart disease, and stable heart failure. ANCOVAs were performed on the total score and on subscores, with sex, age, and education as covariates. RESULTS: Group differences were observed on the MoCA total score (heart failure < low cardiovascular risk), verbal fluency (heart failure < low cardiovascular risk), memory (coronary heart disease < low cardiovascular risk), and orientation (coronary heart disease < low and high cardiovascular risk) subscores. CONCLUSION: Results suggest that the MoCA, particularly verbal fluency and memory subscores, can detect cognitive changes in later stages of the cardiovascular disease continuum, such as heart failure. Detecting cognitive changes earlier on the cardiovascular disease continuum may require more in depth neuropsychological assessments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".