Use of Anticoagulation Therapy in Patients With Perioperative Atrial Fibrillation After Cardiac Surgery: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Perioperative atrial fibrillation (POAF) after cardiac surgery is associated with an increased risk of stroke. However, the efficacy and safety of using anticoagulation therapy in this population are unknown. Methods: We performed a systematic review and meta-analysis of studies comparing use of anticoagulation therapy vs no anticoagulation therapy in patients with POAF after cardiac surgery. Outcomes included arterial thromboembolism (ie, stroke ± systemic embolism) and bleeding. Data were pooled using fixed-effects models. We reported summary risk ratios (RRs) for studies with multivariable adjustment and estimated absolute risk differences with 95% confidence intervals (CIs). Results: < 0.001; 2 studies). The estimated short-term and long-term absolute risk increases in bleeding with use of anticoagulation therapy were 0.5% (95% CI, 0.4-0.6) and 42 events per 1000 person-years (95% CI, 35-51), respectively. Conclusions: Use of anticoagulation therapy is associated with a small reduction in the risk of arterial thromboembolism, but also an increased risk of bleeding. Randomized controlled trials are needed to address this issue.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".