Effects of Atrial Fibrıllation on Cognitive Functions in Patients Between 65-75 Years of Age
Bibliographic record
Abstract
Objective: Atrial fibrillation (AF) is the most common arrhythmia in the elderly population and also the most common cause of ischemic stroke. Ischemic stroke is directly related to cognitive decline. The relationship between atrial fibrillation and cognitive decline has long been associated with stroke. This study aimed to reveal whether the mere presence of atrial fibrillation, independent of stroke, has negative effects on cognitive functions. Material and Method: Male and female patients between the ages of 65 and 75 with no chronic diseases other than known hypertension were included in the study. They were divided into two groups according to electrocardiography findings: the group with newly diagnosed atrial fibrillation and the group with normal sinus rhythm (NSR). To evaluate cognitive functions, the Montreal Cognitive Assessment (MoCA) was applied to both groups and then the groups were compared in terms of scores. Results: No statistically significant difference was observed between the groups in terms of age, patient characteristics, educational status, or laboratory findings. MoCA scores were significantly lower in the AF group than in the NSR group (p=0.001). Multivariable linear regression analysis demonstrated lower age and higher education status were independently associated with high MoCA scores (β: 3.392, 95% CI: 2.375 - 4.410, p
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".