Quality of life in patients with long-standing persistent atrial fibrillation after surgical ablation and simultaneous coronary artery bypass grafting
Bibliographic record
Abstract
Aim. To analyze quality of life (QOL) of patients with coronary artery disease (CAD) in combination with long-standing persistent atrial fibrillation (AF) in the long-term postoperative period, depending on chosen surgical strategy for concomitant pathology. Material and methods. The analysis of QOL changes in the long-term postoperative period (12 and 24 months) in 116 patients with CAD and concomitant long-term persistent AF who selectively underwent biatrial (BA) or isolated left atrial (LA) ablation with simultaneous on-pump coronary artery bypass grafting. To assess QOL, a non-specific Medical Outcomes Study 36-Item Form Health Status Survey (SF-36) questionnaire was used. Patients were questioned in preoperative and long-term postoperative periods (12 and 24 months). Results. All SF-36 parameters significantly improve after open surgical treatment in the long-term postoperative period (24 months) with both treatment strategies (BA and LA ablation) for AF. In the BA ablation group, 74% of patients did not have arrhythmia after 12 months, and only 38,5% of patients in the LA ablation group belonged to European Heart Rhythm Association (EHRA) score class 1 (p=0,001). After 24 months, a comparison revealed a significant diff erence between the two groups in arrhythmia symptoms (p=0,014), with maintaining the advantage of the BA ablation group. After 12 and 24 months, none of the patients in both compared groups had severe class IV angina. Conclusion. SF-36 parameters were improved 24 months after surgical treatment of CAD and long-standing persistent AF, regardless of the ablation strategy. Elimination of angina symptoms and long-term maintenance of sinus rhythm can improve the QOL of patients in the long-term postoperative period.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".