Risk Factors for Atrial Fibrillation following a Cardiac Surgery
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) following cardiac surgery is common and has clinical impact on morbidity. The preoperative and intraoperative risk factors are still not well defined. The objective of the study was to examine preoperative and intraoperative risk factors for AF following cardiac surgery. Methods: A retrospective analysis of a database of cardiac surgeries was performed during 2017-2019 at Poriya Medical Center. Preoperative factors and intraoperative were recorded. Results: 208 patients were included in this analysis. Overall AF following cardiac surgery was detected in 50 (24%) patients. Of 175 patients who did not have history of AF prior to surgery, 27 (15.5%) had post-operative AF. In the 33 patients with previous AF, AF following surgery was detected in 23 (70%). Patients with AF following surgery who were older (66.2±8.0 vs. 60.7± 11.4 years, p=0.002), were treated more with anti-arrhythmic drugs (18.9% vs 4.5, p<0.001), and had higher rates of pre-operative AF (46% vs 6.3%, p=0.0001), prior cerebral vascular accidents (14% vs 4.4%, p=0.019), and prior valve replacement (10% vs 1.9%, p=0.009) compared to patients without AF following surgery. In multivariate Cox regression analysis, age (HR 1.04, CI 1.01-1.07, P=0.006) and history of preoperative AF (HR 6.01, CI 3.42-10.57, P<0.001) were predictors of AF following cardiac surgery. The probability of being free of postsurgical AF was 80% among patients without history of AF compared to 30% in patients with previous AF history (p<0.001). Conclusion: Preoperative AF and age were predictors of AF following cardiac surgery
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".