Management of atrial fibrillation in the emergency department and following acute myocardial infarction.
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common arrhythmia managed by emergency physicians and there is increasing evidence that selected patients with acute AF can be safely managed in the emergency department without the need for hospital admission. The principles of management are identification and treatment of precipitating or underlying causes, hemodynamic stabilization/rate control, reduction of thromboembolism risk and the conversion/maintenance of sinus rhythm. A strategy of rate or rhythm control should be chosen based on the patient's clinical status, the duration of AF, the experience of the treating physician and the status of anticoagulation. Before either electric or pharmacological cardioversion, anticoagulation should be considered. Most patients should be given heparin or low molecular weight heparin while preparing for cardioversion. All patients should be considered for long-term anticoagulation based on their thromboembolic risk and bleeding risk from antithrombotic therapy. Following restoration of sinus rhythm, a decision regarding the use of antiarrhyhmic drugs should be made based on the estimated frequency of recurrence and degree of symptoms. In the setting of acute myocardial infarction, beta-blockers should be administered whenever possible. If beta-blockers are contraindicated, the rate can be slowed with digoxin or amiodarone. Cardioversion should be performed if the patient is hemodynamically unstable. Class IC antiarrhythmic drugs should not be administered in this setting.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".