Management of postoperative atrial fibrillation after cardiac surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: Postoperative atrial fibrillation (POAF) occurs commonly after cardiac surgery and is associated with a number of adverse outcomes. This article will review the available evidence on the prevention and treatment of atrial fibrillation after cardiac surgery. Using this knowledge, we propose a conceptual framework on the management of patients with POAF during various phases after cardiac surgery. RECENT FINDINGS: Perioperative β-blockade is the cornerstone in preventing POAF after cardiac surgery. Results from randomized trials do not support routine use of colchicine or corticosteroids to prevent POAF. There is no study examining the impact of rate versus rhythm control on 'hard' clinical outcomes such as mortality or stroke in the cardiac surgical population. Furthermore, there is a paucity of research on the optimal timing and choice of oral anticoagulation among POAF cardiac surgical patients who are at risk for stroke. SUMMARY: In spite of the plethora of therapies available to treat and prevent POAF in the cardiac surgical population, there is little data to address whether they can improve key clinical outcomes such as death or stroke. Guideline recommendations on rate/rhythm control and oral anticoagulation for stroke prevention in the cardiac surgical population are largely extrapolated from studies of nonsurgical atrial fibrillation patients. Further research is needed to address these key atrial fibrillation management issues specific to the cardiac surgical population.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 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".