Scar-based catheter ablation for persistent atrial fibrillation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Percutaneous catheter ablation can be an effective treatment for paroxysmal atrial fibrillation. However, catheter ablation for the treatment of persistent atrial fibrillation or long-standing persistent atrial fibrillation is associated with success rates of 45-50% at 1 year. To address the challenge of ablating patients with persistent atrial fibrillation, several approaches have been proposed. Atrial scar-based catheter ablation is a promising strategy for ablation of persistent atrial fibrillation. RECENT FINDINGS: In this review, we outline the role of atrial scar/fibrosis in the pathophysiology of atrial fibrillation and how this encouraged clinical studies assessing the atrial substrate using scar-based mapping. We highlight current approaches to voltage mapping of atrial scar in patients with atrial fibrillation. The characteristics, techniques, and outcomes of recently published studies evaluating scar-based catheter ablation strategies for the treatment of atrial fibrillation are discussed. Finally, we explore the role of noninvasive tools such as delayed enhancement MRI to assess the atrial fibrillation substrate. SUMMARY: In summary, the optimal catheter ablation strategy for persistent atrial fibrillation remains unknown. Current data highlight the need for a better understanding of the substrate and mechanisms of arrhythmia maintenance in this population. Atrial scar-based catheter ablation has recently emerged as a promising strategy for ablation of atrial fibrillation. However, the available data have limitations that preclude definitive conclusions regarding the utility of this strategy. Further research is needed to assess the role of scar-based ablation for persistent atrial fibrillation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".