Abstract 9174: Global Variation in the Etiology and Management of Atrial Fibrillation: Results from a Global Atrial Fibrillation Registry
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is the most common arrhythmia worldwide. A truly global registry is needed. Methods: The AF registry enrolled unselected patients who presented to an emergency department with AF. Results: A total of 14,434 patients were enrolled: 45% with AF as their primary diagnosis and 36% with their first documented episode of AF. The number of sites and patients were: North America (18; 1787), Latin America (23; 1116), Western Europe (19; 1903), Eastern Europe (22; 2445), Middle East (8; 813), Africa (20; 891), India (24; 2450), China (20; 1905) and Asia (12; 1124). The average age of patients in North America was 70.2 years and 57.7 % of patients were male. Patients in Africa and India were 12 years younger and were more likely to be female. Hypertension was the most common risk factor, present in 62% of patients worldwide. Its prevalence ranged from 41.5% in India to 80.0% in Eastern Europe. Heart failure was the second most common risk factor, present in 35% of patients worldwide. Its prevalence ranged from 17.4% in India to 70.5% in Africa. Rheumatic heart disease was present in only 2.1% of North American patients with AF, compared to 15.3% in both the Middle East and China, 22.0% in Africa and 30.9% in India. In Africa, more than 5% of AF cases were associated with pericarditis or endomyocardial fibrosis, while these conditions were present in < 1% of patients elsewhere. In North America, the use of oral anticoagulation (OAC) among patients with AF and a CHADS 2 score of ≥ 2 was 72.6%. Elsewhere, the rate was from 19% in China to 63% in Eastern and Western Europe. For patients treated with OAC, the proportion of INR values between 2.0 and 3.0 ranged from 32.6% in India to 66.5% in Western Europe. Conclusions: Although advanced age, hypertension and heart failure are the most common predisposing conditions for AF in all regions, rheumatic heart disease remains an important cause in low-income countries. Appropriate use of OAC is low, particularly in low-income countries.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".