P978Relationship between structural brain damage and cognitive function in patients with atrial fibrillation
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) has been associated with dementia, but information on the association of structural brain lesions with cognitive function is scarce. The aim of this study was to investigate the relationship between structural brain lesions and neurocognitive function in a cohort of unselected AF patients. Methods: Swiss-AF is an ongoing, prospective, multicentre, observational cohort study. Overall, 2,415 patients with documented AF were enrolled at 13 sites in Switzerland. All patients underwent neurocognitive testing using the Montreal Cognitive Assessment (MoCA) score, which is scaled from 0 (worst) to 30 (best). Cerebral magnetic resonance imaging (cMRI) was performed at baseline using a standardized protocol. Volumes of infarcts, microbleeds, lacunes and small vessel disease were measured. To assess the relationship between the log-transformed volume of brain lesions and the MoCA score linear regression analyses were performed. Results: Overall, 1,736 study patients were included in this analysis. Mean age was 73±8 years and 1,261 (73%) were men. The mean CHA2DS2-VASc Score was 3.3±1.7 and 1,559 (90%) patients were on oral anticoagulation. Infarcts, lacunes, microbleeds and small vessel disease were found in 399 (23%), 331 (19%), 370 (21%) and 1709 (98%) patients, respectively. The mean MoCA score was 25.5±3.1. After multivariable adjustment, volume of infarct and volume of small vessel disease remained inversely associated with the MoCA score with β-coefficients (95% CI) of -0.26 [-0.41, -0.11], p<0.001 and -0.12 [-0.23, -0.01], p=0.03, respectively (Table). In a combined model including all brain lesions in one model, volume of infarcts and small vessel disease showed the strongest association with MoCA score (Table).
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".