Systemizing the Evaluation of Acute Heart Failure in the Emergency Department
Bibliographic record
Abstract
ntroduction of the Hospital Readmission Reduction Program, imposing financial penalties for early readmissions after hospital discharge, has created a strong incentive for hospitals to reduce heart failure (HF) admissions, often by risk-stratifying patients before discharge for more intensive follow-up or disease management programs.A less explored option for reducing readmissions is risk-stratifying patients presenting to the emergency department (ED) to identify those who can be managed safely at home.Exploring this opportunity is important, given that the ED plays a pivotal role in HF care, with nearly 80% of patients evaluated being admitted. 1The current decision to admit these patients is based on clinical judgment, with large regional and hospital-level variability in readmission rates. 2 At our institution, the rate of HF discharge from the ED across the 46 ED physicians in the 2016 calendar year was 20% and varied from 0 to 34%.To better identify patients warranting admission, we developed and implemented a standardized care path, Code Heart Failure (CodeHF) for evaluating patients with acute HF presenting to the ED.This report describes the creation and implementation of the CodeHF pathway, along with early insights into its promise and pitfalls. GOALS AND VISION OF THE PROGRAMThe primary goal of this initiative was to improve care for patients with HF exacerbations presenting to the ED through early identification, evidence-based risk stratification, education, and timely follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".