Balancing the Risks of Stroke and Upper Gastrointestinal Tract Bleeding in Older Patients With Atrial Fibrillation
Bibliographic record
Abstract
OBJECTIVE: To determine how factors that increase the risk of major upper gastrointestinal (GI) tract hemorrhage (recent upper GI tract bleeding or concurrent use of nonsteroidal anti-inflammatory drugs) influence the choice of antithrombotic therapy in older patients (those > or = 65 years) with atrial fibrillation. METHODS: For older patients with atrial fibrillation and no other contraindications to antithrombotic therapy, a Markov decision-analytic model was used to determine the preferred treatment strategy (no antithrombotic therapy, long-term aspirin use, or long-term warfarin sodium use) based on their risk of major upper GI tract hemorrhage. Input data were obtained by a systematic review of MEDLINE. Outcomes were expressed as quality-adjusted life-years (QALYs). RESULTS: For 65-year-old patients with average risks of stroke and upper GI tract bleeding, warfarin therapy was associated with 12.1 QALYs per patient; aspirin therapy, 10.8 QALYs; and no antithrombotic therapy, 10.1 QALYs. For persons with significantly higher risks of upper GI tract bleeding and/or lower risks of stroke, warfarin was no longer clearly the optimal antithrombotic therapy (eg, for 80-year-old persons with a baseline risk of stroke of 4.3% per year who were concurrently taking a conventional nonsteroidal anti-inflammatory drug: warfarin, 7.44 QALYs; aspirin, 7.39 QALYs; and no treatment, 7.21 QALYs). CONCLUSIONS: For older patients with atrial fibrillation and factors that place them at a higher than average risk of upper GI tract bleeding, the optimal choice of antithrombotic therapy to prevent stroke can vary according to the magnitude of this risk. Based on the risks of stroke and upper GI tract bleeding, clinicians can use the treatment recommendations of this study to provide rational stroke prevention therapy for older patients with atrial fibrillation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".