Novel Anti-arrhythmic Medications in the Treatment of Atrial Fibrillation
Bibliographic record
Abstract
Atrial fibrillation (AF) is a prevalent condition particularly amongst the elderly, which contributes to both morbidity and mortality. The burden of disease has lead to significant increases in health care utilization and cost in recent years. Treatment of Atrial fibrillation consists of either a rate or rhythm control strategy. Rhythm control is achieved using medical management and/or catheter ablation. In spite of major strides in catheter ablation, this procedure remains a second line treatment of AF. Anti-arrhythmic medications represent the main treatment modality for the maintenance of sinus rhythm. Amiodarone has been used for decades because of its efficacy and lack of pro-arrhythmia despite numerous extracardiac side effects. Novel agents such as Dronedarone were designed to emulate Amiodarone without the extra-cardiac side effects. Unfortunately recent trials have raised concerns for the safety of this medication in certain patients. Other agents such as Vernakalant and Ranolazine are in development that promise to be more atrial selective in their action, thereby potentially avoiding pro-arrhythmia and heart failure side effects. It remains to be seen however if one or more of these agents achieves the required high efficacy and safety threshold. This review summarizes the main anti-arrhythmic clinical trials, early phase trials involving novel agents and examines the conflicting data relating to Dronedarone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".