Cognitive Impairment in Patients with Atrial Fibrillation without Stroke
Bibliographic record
Abstract
Background: Vascular dementia is the second leading cause of dementia worldwide; however, the causation is multifactorial and may be preventable. There is increasing evidence that atrial fibrillation (AF) is independently correlated with cognitive decline. Assessing cognition in an outpatient setting is challenging. Gait speed may be able to transcend language in assessing cognition. We aim to assess cognitive impairment in patients with AF without known history of stroke with gait speed. Methods: This was a prospective, observational study of patients attending cardiology outpatient department. Patients were screened for a history of valvular or nonvalvular AF. Controls were patients without AF. Patients underwent structured interview, Montreal cognitive assessment (MoCA), and gait velocity assessment. Gait velocity and MoCA scores were compared in control and cases using Student's t -test. Results: A total of 189 patients were consented; 88 cases with AF and 101 controls. Mean ± standard deviation age was 60 ± 12 years. The median (interquartile range) gait velocity in patients with AF and nonAF was similar (0.80 [0.65–0.93] m/s vs. 0.80 [0.65–0.93] m/s, P = 0.708). The mean MoCA scores in patients with AF and without AF were also similar (17.38 ± 5.66 vs. 18.36 ± 5.30, P = 0.229). A cutoff value of <0.80 m/s had sensitivity of 66% and specificity of 61.4% to diagnose dementia. Conclusion: There is a high occurrence of cognitive deficits in patients with and without AF visiting a cardiology outpatient clinic. Future studies are needed to target this group of the patient to reduce the burden of vascular dementia.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".