P4800Why do clinicians prescribe oral anticoagulation in patients with atrial fibrillation despite a low CHA2DS2-VASc score?
Bibliographic record
Abstract
Background: Oral anticoagulant (OAC) therapy is prescribed in approximately 40% of patients with atrial fibrillation (AF) and low thromboembolic risk (CHA2DS2-VASc score 0 [male] or 1 [female]). Guidelines recommend against OAC therapy in such patients because the annual thromboembolic risk (<1%) is outweighed by bleeding. Purpose: To identify patient characteristics and reasons for clinicians to prescribe OAC therapy in AF despite a low thromboembolic risk. Methods: Patient characteristics associated with OAC prescription were assessed in the subgroup with a low CHA2DS2-VASc score from the GARFIELD-AF registry. All-cause mortality, ischemic stroke or systemic embolism, and major bleeding were compared according to OAC status. Next, a diverse group of clinicians involved in AF care were questioned through a web-based survey. Items included factors, not included in the CHA2DS2-VASc score, that may influence prescription of OAC therapy in AF. Results: In the GARFIELD-AF registry (n=52,014), 2,123 patients had a low CHA2DS2-VASc score. OAC therapy was prescribed in 950 (45%). Permanent [OR (95%) = 2.32 (1.52–3.56)] or persistent AF [OR (95%) = 3.08 (2.17–4.38)] and increasing age <65 years [OR (95%) = 1.34 (1.20–1.50)] demonstrated a significant increase in odds for OAC use, while concomitant antiplatelet therapy [OR (95%) = 0.083 (0.065–0.105)] and female gender [OR (95%) = 0.714 (0.561–0.907)] showed a significant decrease in odds. Crude event rates were low for those with as well as without OAC therapy: all-cause mortality (14 versus 20), ischemic stroke or systemic embolism (6 versus 5), and major bleeding (4 versus 3). When clinicians (n=229) were questioned about decision-making regarding OAC therapy for AF patients with low thromboembolic risk, an enlarged left atrium or spontaneous echo contrast was the most frequently cited reason (reach: 59.8%). Adding cardioversion or ablation procedures, rheumatic heart disease, and subjective fear of stroke by the patient increased the reach to 83.8% (Table 1).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".