Atrial fibrillation: review of current treatment strategies
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia in modern clinical practice, with an estimated prevalence of 1.5-2%. The prevalence of AF is expected to double in the next decades, progressing with age and increasingly becoming a global medical challenge. The first-line treatment for AF is often medical treatment with either rate control or anti-arrhythmic agents for rhythm control, in addition to anti-coagulants such as warfarin for stroke prevention in patient at risk. Catheter ablation has emerged as an alternative for AF treatment, which involves myocardial tissue lesions to disrupt the underlying triggers and substrates for AF. Surgical approaches have also been developed for treatment of AF, particularly for patients requiring concomitant cardiac surgery or those refractory to medical and catheter ablation treatments. Since the introduction of the Cox-Maze III, this procedure has evolved into several modern variations, including the use of alternative energy sources (Cox-Maze IV) such as radiofrequency, cryo-energy and microwave, as well as minimally invasive thoracoscopic epicardial approaches. Another recently introduced technique is the hybrid ablation approach, where in a single setting both epicardial thoracoscopic ablation lesions and endocardial catheter ablation lesions are performed by the cardiothoracic surgeon and cardiologist. There remains controversy surrounding the optimal approach for AF ablation, energy sources, and lesion sets employed. The goal of this article is review the history, classifications, pathophysiology and current treatment options for 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".