Effectiveness of intra‐operative topical amiodarone for prevention of postcardiac surgery new‐onset atrial fibrillation: A review of current evidence
Bibliographic record
Abstract
BACKGROUND: Postoperative atrial fibrillation (POAF) is one of the most common complications following cardiac surgery and is associated with increased morbidity. Intraoperative topical amiodarone application on epicardial tissue has been shown to reduce systemic concentrations while maintaining therapeutic myocardial concentrations, thereby, lowering the risk of extracardiac adverse effects associated with oral and intravenous amiodarone therapy. However, the efficacy and safety of topical amiodarone in preventing POAF is unclear. OBJECTIVES: This study summarizes the clinical studies to-date that have investigated the efficacy and safety of topical amiodarone administration in preventing POAF following cardiac surgery. METHODS: A database search was conducted using Medline, Embase, and Cochrane Library to identify relevant studies. Abstracts were screened and data were extracted from relevant full-text articles that met the inclusion and exclusion criteria. RESULTS: The search returned four studies with variable findings on the effect of topical amiodarone therapy on the incidence of POAF, cardiac effects, extracardiac effects, and hospital length of stay. CONCLUSION: Prophylactic topical application of amiodarone may be effective and safe for preventing post-operative new-onset atrial fibrillation. Further investigation is required to evaluate the efficacy and safety of topical amiodadrone therapy before it can be widely integrated into current practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".