Postoperative Atrial Fibrillation Following Coronary Artery Bypass Graft Surgery Predicts Long-Term Atrial Fibrillation and Stroke.
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is a common complication of coronary artery bypass graft (CABG) surgery, occurring in 20%-40% of patients, mostly during the first week after surgery. It is associated with increased morbidity and mortality, but data are limited. OBJECTIVES: To assess the correlation between new-onset in-hospital AF following CABG and long-term AF, cerebrovascular accident (CVA), or death. METHODS: We conducted an analysis of 161 consecutive patients who underwent isolated CABG surgery in a tertiary center during the period 2002-2003. RESULTS: Patients' mean age was 72 years, and the majority were males (77%). Approximately half of the patients experienced prior myocardial infarction, and 14% had left ventricular ejection fraction < 40%. Postoperative AF (POAF) occurred in 27% of the patients. Patients were older and had larger left atrium diameter. POAF was strongly correlated with late AF (OR 4.34, 95%CI 1.44-13.1, P = 0.01) during a mean follow-up of 8.5 years. It was also correlated with long-term stroke but was not associated with long-term mortality. CONCLUSIONS: POAF is a common complication of CABG surgery, which is correlated with late AF and stroke. Patients with POAF should be closely monitored to facilitate early administration of anticoagulant therapy in a high risk population upon recurrence of 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".