P5140Geographic region, stroke risk and renal function strongly affect treatment choice for stroke prevention in patients with non-valvular AF: results from the GLORIA-AF registry program
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia and an independent risk factor for stroke. With the introduction of non-Vitamin K antagonist (VKA) oral anticoagulants (NOACs), antithrombotic treatment patterns in non-valvular AF (NVAF) patients have changed. Purpose: To evaluate the effect of stroke and bleeding risk factors on treatment choice of stroke prevention therapy in patients with newly diagnosed NVAF at risk of stroke. Methods: We analyzed data from Phase II of the Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation (GLORIA-AF), which commenced with the first availability of a NOAC in participating countries. Sites were eligible based on availability of both dabigatran and VKA. We predict which treatments for stroke prevention would be prescribed to newly-diagnosed NVAF patients by multivariable multinomial logistic regression based on clinical and demographic factors. Predicted probabilities of receiving each treatment for main predictors are reported, keeping all other characteristics unchanged. Stroke or bleeding risk scores and their component risk factors were assessed in separate models, in both cases adjusting for potential confounders. Predicted probabilities derive from the “components” model, except for the effect of the summary scores.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".