Risk factors for cognitive dysfunction amongst patients with cardiovascular diseases
Bibliographic record
Abstract
Background The impact of cardiovascular diseases on cognition raises important research questions. The study aimed to investigate the relationship between demographic data, cardiovascular diseases, kidney disease and depressive symptoms on cognition. Methods A cross-sectional study of patients with cardiovascular diseases was performed. The Montreal Cognitive Assessment (MoCA) was applied for cognitive evaluation. Based on MoCA three groups were defined: preserved cognition, mild, and advanced cognitive dysfunction (CD). Data were analyzed using Cronbach alpha (Cα) and McDonald’s ω (Mω) for internal consistency. The Chi-square test, Cramer’s V test, and correlation analyses were also applied. Results Of 628 patients, 55.2% had mild CD, and the mean age was 67.95 (SD 9.53) years. Cα and Mω were 0.7, indicating good internal consistency. We found a moderate positive correlation between depression and the severity of CD (r = 0.25, p = 0.0001). A weak association between CD and female gender (p = 0.016), atrial fibrillation (p = 0.03), stroke (p = 0.009), and a moderate association for age group (p < 0.0001), education level (p < 0.0001), smoking (p < 0.0001), and renal dysfunction (p < 0.0001) was found. Age ≥ 70 years, eGFR 30–59 mL/min/1.73m2 significantly increased the likelihood for mild and advanced CD, while smoking and > 9 classes decreased it. Female gender, history of atrial fibrillation, and stroke significantly increased the likelihood of advanced CD. Conclusion Mild CD was the most common in patients with cardiovascular diseases. Older age, lower education, being a non-smoker, and renal dysfunction were risk factors for both mild and advanced CD. Female gender, previous diagnosis of atrial fibrillation, and stroke are risk factors for advanced CD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".