A multi-hospital analysis of predictors of oral anticoagulation prescriptions for patients with actionable atrial fibrillation who attend the emergency department
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common arrhythmia and is associated with an increase in the risk of ischemic stroke. The risk of stroke can be significantly decreased by oral anticoagulation (OAC). Our objective was to characterize the filling of OAC prescriptions for patients with actionable AF (new or existing AF with an indication for OAC but not prescribed) and determine the prevalence and predictors of guideline-appropriate therapy at 30 days.This is a multi-hospital, retrospective cohort study of patients who visited the Emergency Department (ED) and had a discharge diagnosis of AF. Patient records were examined to identify demographics, risk factors, and prescription data. Predictors of filling a prescription at 30 days were analyzed.788 patients with AF were reviewed. 257 patients had actionable AF. Forty one percent (104) had newly diagnosed AF. The mean CHADS2 score was 2 ± 1. At 30 days after discharge, 25.7% of patients filled a prescription for OAC therapy.Large numbers of patients attending the ED have actionable AF, but rates of guideline-directed OAC at thirty days are low. Only a prescription written by the ED physician (OR 9.89) and documentation of stroke risk stratification in the patients’ chart (OR 4.09) were associated with the primary outcome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".