Evolution of antithrombotic therapy for patients with atrial fibrillation: The prospective global GLORIA-AF registry program
Bibliographic record
Abstract
OBJECTIVE: To assess baseline characteristics and antithrombotic treatment (ATT) prescription patterns in patients enrolled in the third phase of the GLORIA-AF Registry Program, evaluate predictors of treatment prescription, and compare results with phase II. METHODS: GLORIA-AF is a large, global, prospective registry program, enrolling patients with newly diagnosed nonvalvular atrial fibrillation (AF) at risk of stroke. Patients receiving dabigatran were followed for two years in phase II, and all patients were followed for 3 years in phase III. Phase II started when dabigatran became available; phase III started when the characteristics of patients receiving dabigatran became roughly comparable with those receiving vitamin K antagonists (VKAs). RESULTS: Between 2014 and 2016, 21,241 patients were enrolled in phase III. In total, 82% of patients were prescribed oral anticoagulation ([OAC]; 59.5% novel/nonvitamin K oral anticoagulants [NOACs], 22.7% VKAs). A further 11% of patients were prescribed antiplatelets without OAC and 7% were prescribed no ATT. A high stroke risk was the main driver of OAC prescription. Factors associated with prescription of VKA over NOAC included type of site, region, physician specialty, and impaired kidney function. CONCLUSION: Over the past few years, data from phase III of GLORIA-AF show that OACs have become the standard treatment option, with most newly diagnosed AF patients prescribed a NOAC. However, in some regions a remarkable proportion of patients remain undertreated. In comparison with phase II, more patients received NOACs in phase III while the prescription of VKA decreased. VKAs were preferred over NOACs in patients with impaired kidney function.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".