Augmenting Atrial Fibrillation Care After an Emergency Department Visit
Bibliographic record
Abstract
BACKGROUND: Between 2010 and 2012, the Heart Rhythm team in a tertiary care hospital completed a retrospective study that found that atrial fibrillation (AF) care can be episodic and heavily reliant on hospital resources, particularly the emergency department (ED). PROBLEM: Patients who attend the ED with AF are at high risk of hospital admission. APPROACH: A nurse practitioner (NP) was added to the Heart Rhythm team to create a program to improve AF care after an ED visit. Telephone practice was one of the many processes created. OUTCOMES: Findings revealed that 37 of 90 patients presented to the ED with AF prior to telephone contact and 7 of 90 patients did so post-telephone contact (P < .001). CONCLUSION: Telephone practice led by an NP provides an opportunity to improve assessment and management of patient with AF and offers a promising cost-effective method to reduce ED visits in the AF patient population.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".