Evolving antithrombotic treatment patterns for patients with newly diagnosed atrial fibrillation
Bibliographic record
Abstract
Objective We studied evolving antithrombotic therapy patterns in patients with newly diagnosed non-valvular atrial fibrillation (AF) and ≥1 additional stroke risk factor between 2010 and 2015. Methods 39 670 patients were prospectively enrolled in four sequential cohorts in the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF): cohort C1 (2010–2011), n=5500; C2 (2011–2013), n=11 662; C3 (2013–2014), n=11 462; C4 (2014–2015), n=11 046. Baseline characteristics and antithrombotic therapy initiated at diagnosis were analysed by cohort. Results Baseline characteristics were similar across cohorts. Median CHA 2 DS 2 -VASc (cardiac failure, hypertension, age ≥75 (doubled), diabetes, stroke (doubled)-vascular disease, age 65–74 and sex category (female)) score was 3 in all four cohorts. From C1 to C4, the proportion of patients on anticoagulant (AC) therapy increased by almost 15% (C1 57.4%; C4 71.1%). Use of vitamin K antagonist (VKA)±antiplatelet (AP) (C1 53.2%; C4 34.0%) and AP monotherapy (C1 30.2%; C4 16.6%) declined, while use of non-VKA oral ACs (NOACs)±AP increased (C1 4.2%; C4 37.0%). Most CHA 2 DS 2 -VASc ≥2 patients received AC, and this proportion increased over time, largely driven by NOAC prescribing. NOACs were more frequently prescribed than VKAs in men, the elderly, patients of Asian ethnicity, those with dementia, or those using non-steroidal anti-inflammatory drugs, and current smokers. VKA use was more common in patients with cardiac, vascular, or renal comorbidities. Conclusions Since NOACs were introduced, there has been an increase in newly diagnosed patients with AF at risk of stroke receiving guideline-recommended therapy, predominantly driven by increased use of NOACs and reduced use of VKA±AP or AP alone. Trial registration number NCT01090362; Pre-results.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".