Secondary Prevention of Nonvalvular Atrial Fibrillation: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Secondary prevention of atrial fibrillation (AF) could be carried out by means of antiarrhythmic drugs; however this strategy has not received any endorsement because these drugs are burdened by a high risk of proarrhythmic events (flecainide, sotalol) or extracardiac effects (amiodarone). METHODS: In our retrospective cohort study we have compared amiodarone 200 mg per day with the strategy implying the renunciation of any specific drug as well as with the approach using oral anticoagulant (rivaroxaban) or a combined approach including amiodarone plus rivaroxaban. RESULTS: A total of 255 patients with a history of AF (paroxysmal, persistent or long-lasting persistent) successfully treated with achievement of sinus rhythm have been gathered. Amiodarone has been the most effective option for AF secondary prevention, with regard to the recurrences of AF as well as rehospitalizations: P (Kruskal-Wallis test) < 0.05 for both, over a median follow-up of 24 months. CONCLUSIONS: Patients kept free from any specific drug therapy have been shown to experience more numerous AF relapses and related rehospitalizations. On the contrary, the amiodarone use has been associated with a decreased risk of AF recurrences and hospital admissions. Thus, amiodarone might be an efficacious tool for realizing a successful long-term AF secondary prevention.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".