Catheter ablation for the management of atrial fibrillation: current technical perspectives
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice, with a prevalence that increases alongside the ageing population worldwide. The management of AF involves restoration of sinus rhythm through antiarrhythmic drug therapy. Yet, these medications have only modest efficacy in achieving long-term success, have not shown to result in a mortality benefit, are frequently not tolerated and have associated adverse side effects. Therefore, catheter ablation has become a valuable treatment approach for AF and even a viable first-line strategy in select cases. Traditionally, the combination of radiofrequency energy and a three-dimensional electroanatomical mapping system has been used to guide catheter ablation for AF. However, single-procedural efficacy and long-term outcomes still remain suboptimal for many patients, particularly those with persistent or long-standing AF. Recent advances in ablation technology and strategy, therefore, provide new procedural approaches for catheter-based treatment with the aim of overcoming current challenges in procedural duration and overall success. The aim of this paper was to provide an updated review of the current practices and techniques relating to ablation for AF and to compare the use of these strategies for paroxysmal and persistent AF.
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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 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".