The Atrial Fibrillation Therapies after ER visit: Outpatient Care for Patients with Acute AF - The AFTER3 Study.
Bibliographic record
Abstract
BACKGROUND: Visits to the emergency room (ER) for atrial fibrillation/flutter (AF) are common, but follow-up care is rarely systematically organized and is often delayed. PURPOSE: We conducted a pilot program to develop a systematic, protocol-based system of care for patients presenting to the ER with a primary diagnosis of AF. METHODS: Consecutive patients presenting to the ER with ECG-documented AF at an urban teaching hospital were treated according to a guideline-based care protocol, including a patient toolkit at ER discharge, and systematic referral to a rapid access AF clinic. Consenting patients received questionnaires on AF knowledge, patient satisfaction, and the AFEQT questionnaire at first visit and three-month follow-up. RESULTS: Of the 321 patients with AF, 244 (76%) were discharged from the ER and 166 (68%) were referred to the AF clinic for urgent follow-up. Among 166 referred, 144 (87%) were seen, within a median 10.5 days (IQR 6-16.5 days); 128 (89%) patients agreed to participate in the study and 81% received a toolkit in the ER. The mean age of patients seen in AF clinic was 63.6±13.2 years and 59% were male. Eighty-seven percent were aware of their diagnosis, stroke risk (82%), possible complications (90%), treatment options (86%) and benefits of adherence (86%). Severity of Atrial Fibrillation class was > 2 in 51% at baseline; AFEQT scores increased from baseline (56.4±25.5) to three months post-ER visit (76.4±20.0), a moderately large improvement in QOL (p<0.0001). Seventy eight percent of patients with CHA2DS2-VASc score > 1 were treated with an oral anticoagulant. CONCLUSIONS: A systematic program to improve patient transition of care from the ER to community clinic was associated with prompt, guideline-based care, and high levels of patient disease awareness. Quality of life scores improved substantially between the index ER visit and 3 months post-visit.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".