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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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".