Abstract 13565: Patterns of Newly Detected Atrial Fibrillation and Antithrombotic Treatment in GLORIA-AF Phase II
Bibliographic record
Abstract
Purpose/Background: Anticoagulation with Vitamin K antagonists (VKAs) or non-VKA oral anticoagulants (NOACs) is recommended for patients with atrial fibrillation (AF) and additional risk factors for stroke, regardless of the pattern of AF. Using an inception cohort design, GLORIA-AF collected data on antithrombotic therapy choices for patients with newly identified AF at risk of stroke in the course of routine care. Methods: Phase II began after the first NOAC became available for stroke prevention in AF and baseline data were recorded from November 2011 through December 2014 in 44 countries worldwide. To reduce selection bias, patients were recruited consecutively, irrespective of antithrombotic therapy received. Antithrombotic treatment is presented in relation to CHA 2 DS 2 VASc scores and the pattern of AF. Results: In phase II of GLORIA-AF, 15,092 patients with a recent diagnosis of AF and CHA 2 DS 2 VASc scores ≥ 1 were enrolled. Median age was 71 years and 39.1% were ≥ 75 years old; 45.5% were female, 74.6% had a history of hypertension, 23.1% diabetes mellitus and 86.1% had CHA 2 DS 2 VASc scores ≥ 2. AF was classified as paroxysmal in 53.4% (n = 8052), persistent in 35.5% (n = 5362) and permanent in 11.1% (n = 1678) of patients. Relationships of initial antithrombotic therapy to AF pattern and stroke risk are shown in the figure below and further data on regional differences will be presented. Conclusion: Initial antithrombotic therapy diverged from guideline recommendations. Patients with paroxysmal AF were less often anticoagulated than those with persistent or permanent AF, and nearly 22% of patients with paroxysmal AF at high stroke risk were untreated, or given aspirin or other antiplatelet therapy alone.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".