Systematic review and network meta-analysis of stroke-prevention treatments in patients with atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: In the last 4 years, four novel oral anticoagulants have been developed as alternatives to warfarin and antiplatelet agents for stroke prevention in atrial fibrillation (AF) patients. The objective of this review was to estimate the comparative effectiveness of all antithrombotic treatments for AF patients. MATERIALS AND METHODS: Data sources were Medline Ovid (1946 to October 2015), Embase Ovid (1980 to October 2015), and the Cochrane Central Register of Controlled Trials (CENTRAL, Issue 9, 2015). Randomized controlled trials of AF patients were selected if they compared at least two of the following: placebo, aspirin, aspirin and clopidogrel combination therapy, adjusted-dose warfarin (target international normalized ratio 2.0-3.0), dabigatran, rivaroxaban, apixaban, and edoxaban. Bayesian network meta-analyses were conducted for outcomes of interest (all stroke, ischemic stroke, myocardial infarction, overall mortality, major bleeding, and intracranial hemorrhage). RESULTS: Based on 16 randomized controlled trials of 96,826 patients, all oral anticoagulants were more effective than antiplatelet agents at reducing the risk of ischemic stroke and all strokes. Compared to warfarin, dabigatran 150 mg (rate ratio 0.65, 95% credible interval 0.52-0.82) and apixaban (rate ratio 0.82, 95% credible interval 0.69-0.97) reduced the risk of all strokes. Dabigatran 150 mg was also more effective than warfarin at reducing ischemic stroke risk (rate ratio 0.76, 95% credible interval 0.59-0.99). Aspirin, apixaban, dabigatran 110 mg, and edoxaban were associated with less major bleeding than warfarin. CONCLUSION: All oral anticoagulants reduce the risk of stroke in AF patients. Some novel oral anticoagulants are associated with a lower stroke and/or major bleeding risk than warfarin. In addition to the safety and effectiveness of drug therapy, as reported in this study, individual treatment recommendations should also consider the patient's underlying stroke and bleeding risk profile.
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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 | 0.025 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".