Association of mild cognitive impairment with coronary artery disease in elderly patients
Bibliographic record
Abstract
Objective To investigate the relationship between coronary artery disease, other vascular risk factors and mild cognitive impairment in elderly patients. Methods A total of 196 inpatients were collected from the departments of cardiovascular internal medicine from April to September 2015.The unified questionnaires of cognitive status and standardized survey were developed. The patients were divided into 133 cases of mild cognitive impairment and 63 cases of cognitively normal by Montreal Cognitive Assessment Scale. The patients were divided into 79 cases of coronary heart disease and 117 cases of negative control by coronary angiography. Coronary artery disease and other vascular risk factors for mild cognitive impairment were analyzed. Results The levels of education and proportion of coronary heart disease were (9.23±4.34) years and 45.9%(61/133) in mild cognitive impairment patients, and (12.27±3.79) years and 28.6% (18/63) in cognitively normal patients. There were significant differences between the two(t = 4.77, P< 0.05, χ2=5.31, P< 0.05). Multivariate Logistic regression analysis showed that degree of education was a protective factor for mild cognitive impairment with odds ratio (OR) 0.86, 95% confidence interval (CI) 0.77 to 0.91 and P = 0.00. Coronary heart disease was an independent risk factor for mild cognitive impairment (OR=2.02, 95%CI 1.03 to 4.00; P = 0.04) . The total scores of Montreal Cognitive Assessment Scale and delayed memory were (20.9±4.8), (22.4±5.6) points and (2.1±1.6), (2.6±1.8) points in coronary heart disease and negative control patients. There were significant differences between the two (t=1.98, 4.77, all P<0.05). Conclusions The degree of education is a protective factor for mild cognitive impairment. Coronary heart disease is an independent risk factor for mild cognitive impairment, and it mainly impairs delayed memory ability. Key words: Coronary artery disease; Cognition disorders; Risk factors; Neuropsychological tests; Aged
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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 | 0.000 | 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".