Apixaban for Stroke Prevention in Atrial Fibrillation: Why are Event Rates Higher in Clinical Practice than in Randomized Trials?—A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Recent reports suggest an important contribution from frequent off-label use of apixaban 2.5 mg twice daily to the higher rates of thromboembolic events observed in observational studies (OSs) relative to in randomized controlled trials (RCTs), and consequently, advocate against such use in all patients. OBJECTIVES: To examine factors contributing to the higher thromboembolic event rates, we estimated the prevalence of off-label use in contemporary practice, and compared patient characteristics and rates of stroke/systemic embolism, major bleeding, and mortality by apixaban dose and by study design in a systematic review and meta-analysis. RESULTS AND DISCUSSION: -VASc scores (mean 3.6 vs. 2.9) versus those in RCTs. We observed a consistent pattern of higher rates of thromboembolic events, bleeding, and mortality in patients treated with 2.5 versus 5 mg twice daily apixaban in both OSs and RCTs. CONCLUSION: The higher risk profiles of patients in OSs versus RCTs, and higher rates of both bleeding and mortality not attributable to thromboembolism in patients treated with apixaban 2.5 versus 5 mg twice daily suggest that differences in patient characteristics are additional important contributors to the higher than expected thromboembolic event rates in clinical practice.
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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 | 0.020 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".