Prophylactic therapy to prevent atrial arrhythmia after cardiac surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: Atrial fibrillation after cardiac surgery is associated with adverse outcomes and increased costs. Accordingly, therapy should be provided to prevent postoperative atrial fibrillation. The evaluation of therapies to do so is an area of active investigation with significant recent advances. The purpose of this review is to summarize these recent advances in the context of our previous knowledge base regarding the prevention of postoperative atrial fibrillation. RECENT FINDINGS: Recent evaluations of therapy to prevent postoperative atrial fibrillation have raised the prominence of prophylactic amiodarone, redefined the efficacy of prophylactic standard beta-blockers in contemporary cardiac surgical populations, provided further evidence for the use of prophylactic sotalol, magnesium, and atrial pacing, and identified new approaches, including the use of combination therapy, for the prevention of postoperative atrial fibrillation. SUMMARY: According to newly released ACC/AHA/ESC guidelines, use of standard beta-blockers or amiodarone to prevent postoperative atrial fibrillation have a level of evidence of A. Use of prophylactic sotalol has a level of evidence of B, while the use of prophylactic intravenous magnesium or atrial pacing has a lower level of evidence. The use of novel and combination therapies continues to be an area of active investigation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".