P2896Two-year outcomes of dabigatran etexilate in patients with atrial fibrillation with and without a history of coronary artery disease: data from GLORIA-AF
Bibliographic record
Abstract
Background: Patients with atrial fibrillation (AF) have a high prevalence of coronary artery disease (CAD) ranging from 18% to 47%, due to common risk factors such as older age, hypertension and diabetes. Oral anticoagulation is required for AF patients with moderate-to-high stroke risk. The safety and effectiveness of dabigatran etexilate (dabigatran) for stroke prevention in AF has been shown in randomized trials and numerous database studies. Prospective data from routine clinical practice are less common. Purpose: This analysis from the global registry program GLORIA-AF describes clinical outcomes of dabigatran for up to 2 years in newly diagnosed AF patients with or without history of CAD. Methods: GLORIA-AF is a prospective, observational global registry of patients with newly diagnosed AF and a CHA2DS2-VASc score of ≥1. Patients prescribed dabigatran at baseline were followed for up to 2 years. CAD is defined here as history of coronary artery disease, myocardial infarction or angina pectoris. Baseline characteristics and event rates (incidence rates with 95% CI) in patients on dabigatran with and without a history of CAD are reported.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".