Insights from clinical trials: New evidence supports surgical interventions over drug therapies for atrial fibrillation
Bibliographic record
Abstract
Atrial fibrillation (AF) is one of the world’s most prevalent cardiac arrhythmias. It poses a heavy burden on patients, physicians and the global healthcare system as it is one of the top leading causes of cardiovascular death. Researchers have spent numerous years conducting clinical trials to investigate the effectiveness, cost and practicality of treatment for patients suffering from AF. The primary treatment strategy for AF (acute, chronic, persistent, paroxysmal, non-valvular, nonrheumatic, and rapid) involves the use of antiarrhythmic drugs (AAD) and anticoagulant drugs (ACD) to manage heart rate and rhythm, as well as to prevent strokes. This review aims to discuss clinical trials that compared AADs (class Ia: quinidine; class Ic: flecainide, propafenone; class III: sotalol, amiodarone) and ACDs (vitamin K antagonist: warfarin; factor Xa inhibitor: apixaban, rivaroxaban; thrombin inhibitor: dabigatran) with cardiovascular surgical interventions (i.e., catheter ablation, cryoballoon ablation, ablation and DDDR pacemaker, electrical cardioversion, and left atrial appendage occlusion) to treat various types of AF in patients with a diverse history of cardiovascular diseases and medical history. This study provides a review of clinical trials on this topic and enables healthcare professionals to determine the best-suited treatment for their patients.
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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 | 0.085 | 0.270 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 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".