Stroke‐prevention strategies in North American patients with atrial fibrillation: The GLORIA‐AF registry program
Bibliographic record
Abstract
BACKGROUND: Antithrombotic prophylaxis with oral anticoagulation (OAC) substantially reduces stroke and mortality in patients with atrial fibrillation (AF). HYPOTHESIS: Analysis of data in the Global Registry on Long-Term Antithrombotic Treatments in Patients With Atrial Fibrillation (GLORIA-AF), an international, observational registry of patients with newly diagnosed AF, can identify factors associated with treatment decisions and outcomes. METHODS: Multivariable regression identified patient, physician, and temporal factors associated with OAC prescription, compared with management with antiplatelet drugs or no antithrombotic drugs in North American patients enrolled between November 2011 and February 2014. RESULTS: -VASc ≥2), 79.3%, 12.5%, and 7.4% received OAC, antiplatelet drugs, or no antithrombotic therapy, respectively. Of those prescribed OAC, 66.4% received non-vitamin K antagonist oral anticoagulation and 24.5% received concomitant therapy with antiplatelet drugs. Independent predictors of OAC therapy were nonparoxysmal AF (odds ratio, 95% confidence interval: 2.02, 1.56-2.63), prior stroke/transient ischemic attack (2.00, 1.37-2.92), specialist care (1.50, 1.04-2.17), more concomitant medications (1.47, 1.13-1.92), commercial insurance (1.41, 1.07-1.85), and heart failure (1.44, 1.07-1.92). Antiplatelet drugs (0.18, 0.14-0.23), prior falls (0.41, 0.27-0.63), and prior bleeding (0.50, 0.35-0.72) were inversely associated with OAC prescription. CONCLUSIONS: -VASc ≥2 did not receive OAC therapy. Patient characteristics associated with a lower likelihood of OAC prescription were use of antiplatelet drugs, paroxysmal pattern of AF, history of falls, and prior bleeding.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".