Impact of atrial fibrillation on long-term survival after cardiac valve surgery with or without coronary artery bypass
Bibliographic record
Abstract
BACkgRouNd: Atrial fibrillation (AF) is the most common arrhythmia in patients undergoing cardiac valve surgery.AF in patients undergoing surgery can be categorized as preoperative AF (PPAF) or postsurgical AF (PSAF).oBjeCtIVe: To determine whether PSAF in patients undergoing valve surgery had an impact on mortality compared with patients in sinus rhythm or PPAF.MethodS: A total of 556 consecutive patients who underwent valve surgery were reviewed.Patients were divided into three cohorts: sinus rhythm before and after surgery (n=293); PPAF (n=139); and sinus rhythm before and AF after the surgery (PSAF) (n=124).Baseline characteristics, surgical details and outcomes were recorded.ReSuLtS: Compared with patients in sinus rhythm (mean [± SD] age 67.8±12.5 years), patients in the PPAF and PSAF groups were significantly older (73.1±9.9 years and 72.4±9.9 years, respectively).Hospital stay was significantly longer in the PPAF and PSAF groups (10.5±6.1 days and 11.3±8.3days, respectively) compared with patients with sinus rhythm (7.12±4.9days).During a follow-up of 51 months, all-cause mortality was significantly higher in both the PPAF and PSAF groups.This was irrespective of concomitant coronary bypass surgery.On multivariate Cox regression analysis, the adjusted risk for all-cause mortality for PPAF and PSAF was 1.93 (95% CI 1.18 to 3.17; P=0.01) and 1.64 (95% CI 1.07 to 2.53; P=0.02), respectively.CoNCLuSIoN: Patients with PSAF and PPAF have longer hospital stays and higher long-term mortality rates than patients in sinus rhythm.Long-term mortality was similar between PPAF and PSAF.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".