Design of the Swiss Atrial Fibrillation Cohort Study (Swiss-AF): structural brain damage and cognitive decline among patients with atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: Several studies found that patients with atrial fibrillation (AF) have an increased risk of cognitive decline and dementia over time. However, the magnitude of the problem, associated risk factors and underlying mechanisms remain unclear. METHODS: This article describes the design and methodology of the Swiss Atrial Fibrillation (Swiss-AF) Cohort Study, a prospective multicentre national cohort study of 2400 patients across 13 sites in Switzerland. Eligible patients must have documented AF. Main exclusion criteria are the inability to provide informed consent and the presence of exclusively short episodes of reversible forms of AF. All patients undergo extensive phenotyping and genotyping, including repeated assessment of cognitive functions, quality of life, disability, electrocardiography and cerebral magnetic resonance imaging. We also collect information on health related costs, and we assemble a large biobank. Key clinical outcomes in Swiss-AF are death, stroke, systemic embolism, bleeding, hospitalisation for heart failure and myocardial infarction. Information on outcomes and updates on other characteristics are being collected during yearly follow-up visits. RESULTS: Up to 7 April 2017, we have enrolled 2133 patients into Swiss-AF. With the current recruitment rate of 15 to 20 patients per week, we expect that the target sample size of 2400 patients will be reached by summer 2017. CONCLUSION: Swiss-AF is a large national prospective cohort of patients with AF in Switzerland. This study will provide important new information on structural and functional brain damage in patients with AF and on other AF related complications, using a large variety of genetic, phenotypic and health economic parameters.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".