REVIEW: New Approaches to Atrial Fibrillation Management: Treat the Patient, not the ECG
Bibliographic record
Abstract
Atrial fibrillation causes a significant burden on patients and the health care system. The main goals of atrial fibrillation therapy are to improve symptoms and reduce morbidity. There have been significant recent developments in both stoke prophylaxis and rhythm/rate control. The results of the ACTIVE W study emphasize the importance of effective oral anticoagulant therapy in patients with moderate-to-high risk for stroke. The RE-LY study showed superiority of dabigatran, an oral direct thrombin inhibitor, over warfarin in the prevention of stroke, or systemic embolism. Dronedarone, a new antiarrhythmic drug with multiple class effects, has been recently approved by the US Food and Drug Administration for the treatment of atrial fibrillation. Dronedarone has moderate rhythm and rate control efficacy; however, dronedarone significantly reduced cardiovascular hospitalization, cardiovascular death, and stroke in the large ATHENA trial. There is also an important shift in the paradigm of the goals of atrial fibrillation therapy. Instead of focusing solely on the electrocardiographic outcomes of treatment and considering "rhythm versus rate control," one needs to consider "symptom control" as well as patient well-being. This review will suggest that patient based outcomes rather than ECG-based outcomes should be the primary goals of treatment. Original reports and reviews on specific topics were identified through Medline. Randomized controlled trials were selected as the primary source of information. Analysis included critical review of the evidence available to date.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".