Physician Stated Atrial Fibrillation Management in Light of Treatment Guidelines: Data From an International, Observational Prospective Survey
Bibliographic record
Abstract
BACKGROUND: The Registry on Cardiac Rhythm Disorders Assessing the Control of Atrial Fibrillation (RecordAF) study is the first worldwide, prospective, survey of real-life management of atrial fibrillation (AF) in recently diagnosed patients (n = 5604) with a 1-year follow-up. HYPOTHESIS: Surveys of AF management have identified a divergence between guidelines and clinical practice, and an overinterpretation of guidelines in low-risk patients. METHODS: : Physicians' theoretical approaches to rhythm and rate control were investigated using a pre-study questionnaire. RESULTS: One cardiologist, from each of the 583 sites in 6 regions, completed a questionnaire on their practice and management of AF patients. In AF patients with structural heart disease (SHD), amiodarone was the most frequent choice of first-line rhythm control agents in all regions. Amiodarone or sotalol tended to be the preferred second-line rhythm control agents, 1 exception being Central/South America. beta-Blockers were the first-line rate control agents for patients with AF and SHD in all regions, and calcium channel blockers and cardiac glycosides were the most common second-line rate control treatments in all regions, except Asia. In lone AF patients, propafenone (30.6%), flecainide (24.1%), and amiodarone (21.7%) were the most common global choices of first-line rhythm control, and amiodarone or sotalol were the preferred second-line rhythm control agents, 1 exception being Central/South America. CONCLUSIONS: These results highlight points of divergence from the American College of Cardiology (ACC)/ American Heart Association (AHA)/European Society of Cardiology (ESC) guidelines for the management of AF in terms of first-line drug selection in patients with associated SHD or coronary artery disease.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".