Risk factors of atrial fibrillation complicated with cognitive impairment and the relationship between cardiac function parameters and the degree of cognitive impairment
Bibliographic record
Abstract
OBJECTIVE: To explore the risk factors of Atrial Fibrillation (AF) with Cognitive Impairment (CI) and to analyze the relationship between cardiac function parameters and the degree of CI in patients. METHODS: 120 AF patients were selected, and Montreal Cognitive Assessment (MoCA) was used to distinguish between AF patients with and without CI. Univariate analysis and multivariate Logistic regression analysis were used to evaluate the impact of sociodemographic data, disease-related data, and clinical data on risk factors for AF with CI. Pearson's method was used to analyze the correlation between cardiac function parameters and cognitive function scores in AF patients. RESULTS: There were 89 patients with CI and 31 patients without CI, and the MoCA scores of patients with CI were lower than those in patients without CI. Age, occupational status, educational level, combined smoking history, drinking history, and heart failure, as well as systolic blood pressure, diastolic blood pressure, total cholesterol, triglyceride, C-reactive protein, free thyroxine, free triiodothyronine, and D-dimer were risk factors for the patient with CI. Left atrial diameter, left ventricular end-diastolic diameter, left ventricular end-systolic diameter, and left atrial maximum volume in patients with CI were higher than those in patients without CI, and left ventricular ejection fraction and peak early diastolic velocity/peak late-diastolic mitral velocity ratio were lower. CONCLUSION: The cardiac function parameters of patients are closely related to attention, orientation, memory, visuospatial, and executive ability. Cardiac function parameters were closely related to cognitive functions.
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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".