Predictors and impact of postoperative atrial fibrillation following thoracic surgery: a state‐of‐the‐art review
Bibliographic record
Abstract
This review of 19 studies (39,783 patients) of atrial fibrillation after thoracic surgery addresses the pathophysiology, incidence, and consequences of atrial fibrillation in this population, as well as its prevention and management. Interestingly, atrial fibrillation was most often identified in patients not previously known to have the disease. Rhythm control with amiodarone was the most commonly used treatment and nearly all patients were discharged in sinus rhythm. Major predictors were age; male sex; history of atrial fibrillation; congestive heart failure; left atrial enlargement; elevated brain natriuretic peptide level; and the invasiveness of procedures. Overall, patients with atrial fibrillation stayed 3 days longer in hospital. We also discuss the importance of standardising research on this subject and provide recommendations that might mitigate the impact postoperative atrial fibrillation on hospital resources.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".