Persistence with Anticoagulation for Atrial Fibrillation: Report from the GLORIA-AF Phase III 1-Year Follow-up
Bibliographic record
Abstract
BACKGROUND: We aimed to assess the extent to which drug persistence is better with non-vitamin K antagonist oral anticoagulants (NOACs) than vitamin K antagonists (VKAs) in atrial fibrillation (AF) patients and to estimate the difference in therapy persistence depending on NOAC dosing regimen (once daily (QD) vs. twice daily (BID)). METHODS: Consecutive patients were followed for 1 year in phase III of the GLORIA-AF registry. Drug persistence was defined as the use of OAC without any discontinuation in >30 days or switching to alternative therapy. RESULTS: Among 21,109 eligible patients in phase III, 17,266 patients who were prescribed OAC at baseline and those who took ≥1 OAC dose were included. The 1-year proportion of patients receiving NOAC and VKA who persisted on treatment was 80% and 75%, respectively. The 1-year persistence with NOACs BID and NOACs QD was 81% and 80%, respectively. Female gender, hypertension, older age, alcohol use, permanent, asymptomatic, and minimally symptomatic AF were associated with better OAC persistence. Region, medication usage predisposing to bleeding, being a current smoker, treatment reimbursement, and proton pump inhibitors were associated with lower OAC persistence. CONCLUSIONS: Drug persistence was higher with NOACs (1-year persistence was 80%) than with VKAs (75%). There was little difference in 1-year persistence between NOAC dosing regimens.
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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 | 0.005 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".