Hybrid ablation for atrial fibrillation: the importance of achieving transmurality and lesion validation
Bibliographic record
Abstract
Therapeutic ablation for atrial fibrillation (AF) has evolved significantly with progressive advancements in technology and surgical instruments. With the goal of minimizing surgical morbidity while maintaining the benefits of the traditional Cox-Maze procedure, surgical ablation for AF has undergone significant modifications. Most recently, an increased understanding of substrate complexity, predominantly in patients with persistent or long-standing persistent AF, has led to the development of a synergistic hybrid approach. The hybrid approach attempts to combine the benefits of epicardial ablation and catheter-based endocardial ablation in order to overcome the shortcomings associated with each technique alone. Importantly, the aid of electrophysiological intervention has provided new opportunities for evaluating lesion transmurality both acutely and in a staged approach. Therefore, the hybrid procedure may provide the optimal approach for the surgical treatment of AF, with the potential to tailor procedural treatment according to the patient's specific needs. In this review, we aim to provide an overview of current surgical techniques, including the implications of this novel hybrid approach in the management of AF and improving procedural outcomes. Recent findings from published studies are highlighted with a primary focus on the importance of lesion transmurality and validation in a hybrid setting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".