Atrial Fibrillation After Pulmonary Transplantation
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is common after thoracic surgery. Limited data exist concerning the incidence of AF, its impact on mortality, the effectiveness of therapy, and the risk factors of AF after pulmonary transplantation. METHODS AND RESULTS: We reviewed the medical files of 224 consecutive lung transplant recipients who underwent surgery over a 10-year period at a large Canadian center. We collected patient characteristics, in-hospital treatments, and outcomes. Time-to-event analysis was used to account for in-hospital follow-up and models generated to assess the impact of AF on mortality and independent risk factors of AF after transplantation. Postoperative AF occurred in 65 patients (29%). AF was more likely to occur with complications such as pneumonia, mediastinitis, and bronchial dehiscence and was not an independent risk factor of mortality (hazard ratio=1.56; 95% confidence interval, 0.52-4.63). Pharmacological or electric therapy for rhythm or rate control of AF was administered to 97% of patients. Intravenous amiodarone was used in 46%, electric cardioversion in 28%, and heparin in 26%. Only 1 patient remained in AF at discharge. Age (hazard ratio=1.08 by year; 95% confidence interval, 1.05-1.12), bilateral transplantation (hazard ratio=1.87; 95% confidence interval, 1.03-3.42), and a history of AF before the transplantation (hazard ratio=4.48; 95% confidence interval, 1.05-19.11) were found to be independently associated with an increased incidence of postoperative AF. CONCLUSIONS: AF is fairly common after pulmonary transplantation, transient, and relatively benign. It is not independently associated with increased in-hospital mortality. Most patients return to sinus rhythm before discharge. Age, prior AF, and bilateral transplantation increase the risk of postoperative AF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".