Impact of Catheter Ablation of Atrial Fibrillation on Disease Progression
Bibliographic record
Abstract
Atrial fibrillation (AF) remains a major public health challenge worldwide with a globally increasing prevalence and exponential increase in health care costs. The progression from paroxysmal (defined as self-terminating episodes of AF lasting <7 days) to persistent AF (eg, AF episodes lasting longer than 7 days) is associated with premature mortality, increasing incidence of thromboembolism and heart failure, as well as increased rates of hospitalization and health care use. Given recognition that complications of AF increase as the disease advances, there is an urgent need to ensure therapeutic interventions are capable of delaying or halting disease progression. Whereas pharmacotherapy can be relatively effective at managing the symptoms associated with AF, antiarrhythmic drugs are less effective than catheter ablation in reducing arrhythmia burden, improving quality of life, and reducing health care use. Moreover, pharmacologic therapy does not modify the pathophysiological processes responsible for disease progression. Catheter ablation confers a more comprehensive disease-modifying intervention, targeting multiple mechanisms underlying AF progression through a combination of trigger elimination, electroanatomical substrate modification, and autonomic nervous system modulation. Until recently, the belief that catheter ablation was an effective method to prevent disease progression was mostly speculative. However, recent randomized controlled trials have established catheter ablation as disease-modifying intervention. Given this knowledge, it appears that early intervention is critical to optimally affect the disease progression. The purpose of this paper is to review the rationale and evidence supporting disease modification using catheter ablation as a key part of the AF treatment paradigm.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".