Effectiveness of Catheter and Standalone Surgical Ablation Procedures for Atrial Fibrillation: A Bayesian-Network Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Ablation procedures for atrial fibrillation (AF), including catheter (CA) and surgical ablation, are effective rhythm control therapies. This study is a Bayesian network meta-analysis evaluating randomised evidence on the invasive treatment of AF, focusing on freedom from atrial tachyarrhythmias (ATAs) while evaluating the potential trade-off in morbidity and mortality. METHODS: This study was registered in PROSPERO (CRD42025632171). Randomised controlled trials (RCTs) were included comparing any of the 4 treatments; CA, isolated thoracoscopic, hybrid thoracoscopic ablation, and the Convergent procedure. Primary outcome was freedom from ATA at 12 months. Secondary outcomes were mortality, stroke, and bleeding. A Bayesian-network meta-analysis was performed. The combined effects of the primary and secondary outcomes were studied in a bivariate analysis. Treatments were ranked and their effects were summarised using surface under the cumulative ranking curves (SUCRAs). RESULTS: Ten RCTs were included in the analysis (n = 877 patients, predominantly persistent AF). Using CA as a reference, the pooled network odds ratios for freedom from ATA for hybrid thoracoscopic, isolated thoracoscopic, and Convergent were 4.95 (95% credible interval [CrI] 2.16-13.46), 2.23 (95% CrI 1.23-4.48), and 2.23 (95% CrI 0.90-6.69), with SUCRAs for hybrid thoracoscopic, isolated thoracoscopic, Convergent, and CA of 95.5%, 50.8%, 52.1%, and 1.5%, respectively. No increase in periprocedural morbidity or mortality was observed. Results were robust across various sensitivity analyses. CONCLUSIONS: In this Bayesian-network meta-analysis consisting exclusively of randomised data, surgical ablation in general and hybrid ablation in particular provide superior outcome in terms of 1-year freedom from ATA. Both CA and surgical procedures are characterised by a favourable safety profile.
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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 | 0.041 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.054 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".