REVIEW: New Approaches to Atrial Fibrillation Management: Treat the Patient, not the ECG
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it