The techniques for catheter ablation of paroxysmal and persistent atrial fibrillation: a systematic review
Bibliographic record
Abstract
PURPOSE OF REVIEW: Ablation for atrial fibrillation has become a widely accepted and practiced treatment for this arrhythmia. However, the technique for ablation has evolved over time, particularly for the two distinct groups of paroxysmal and persistent atrial fibrillation. This review outlines the optimal techniques for ablation of these different subgroups of atrial fibrillation. RECENT FINDINGS: The most commonly applied techniques for atrial fibrillation ablation include isolation of the pulmonary venous antra, left-atrial linear ablation, and ablation of complex fractionated electrograms (CFEs) during atrial fibrillation. For patients with paroxysmal atrial fibrillation, isolation of the pulmonary venous antra appears to be sufficient, with effective and reproducible outcomes being reported across many centers. For persistent atrial fibrillation, the outcome of catheter ablation is less efficacious. It is widely believed that additional ablation targeting the substrate of atrial fibrillation maintenance is required beyond pulmonary venous isolation. Linear and CFE ablation have been the most commonly employed adjuvant strategies employed, but neither has been adequately compared with the other to make definitive recommendations. SUMMARY: Pulmonary venous antral isolation is the cornerstone of ablation of both paroxysmal and persistent atrial fibrillation. However, to obtain better outcomes in persistent atrial fibrillation, further adjuvant ablation, in the form of either linear or CFE ablation, will likely have to be performed to achieve comparable success rates.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".