Creation and Implementation of an Outpatient Pathway for Atrial Fibrillation in the Emergency Department Setting: Results of an Expert Panel
Bibliographic record
Abstract
Atrial fibrillation and flutter (AF) is a common condition among emergency department (ED) patients in the United States. Traditionally, ED care for primary complaints related to AF focus on rate control, and patients are often admitted to an inpatient setting for further care. Inpatient care may include further telemetry monitoring and diagnostic testing, rhythm control, a search for identification of AF etiology, and stroke prophylaxis. However, many patients are eligible for safe and effective outpatient management pathways. They are widely used in Canada and other countries but less widely adopted in the United States. In this project, we convened an expert panel to create a practical framework for the process of creating, implementing, and maintaining an outpatient AF pathway for emergency physicians to assess and treat AF patients, safely reduce hospitalization rates, ensure appropriate stroke prophylaxis, and effectively transition patients to longitudinal outpatient treatment settings from the ED and/or observation unit. To support local pathway creation, the panel also reached agreement on a protocol development plan, a sample pathway, consensus recommendations for pathway components, sample pathway metrics, and a structured literature review framework using a modified Delphi technique by a technical expert panel of emergency medicine, cardiology, and other stakeholder groups.
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.
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 | 0.215 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".