MétaCan
Menu
Back to cohort

Systemizing the Evaluation of Acute Heart Failure in the Emergency Department

2020· article· en· W3088386222 on OpenAlexfundno aff
Vittal Hejjaji, Alison Scholes, Kevin F. Kennedy, Brett W. Sperry, Yevgeniy Khariton, Evelyn Dean, Douglas S. Lee, John A. Spertus

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2020
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
FundersUniversity of Missouri-Kansas CityNational Heart, Lung, and Blood InstituteSaint Luke's Health SystemUniversity of MissouriHeart and Stroke Foundation of Canada
KeywordsEmergency departmentSAINTHeart failureMedicineLibrary scienceGerontologyHistoryInternal medicineArt historyNursingComputer science

Abstract

fetched live from OpenAlex

Introduction 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.1 The 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. LOCAL CHALLENGES IN IMPLEMENTATIONDespite leadership by an Emergency physician (Dr Scholes), it was well recognized that there would be resistance to practice change due to poor coordination between medical specialties, lack of infrastructure for an evidence-based, standardized method of triage, minimal education for acute HF in the ED, and the perception that HF management had not been identified as an important opportunity for quality improvement.To address this, a multidisciplinary team was formed that involved all relevant stakeholders (ED physicians, cardiologists, HF disease management nurses, administration, and researchers).Serial biweekly meetings were held to design the intervention and oversee its implementation.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.110
GPT teacher head0.371
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCirculation Cardiovascular Quality and OutcomesSame topicEmergency and Acute Care StudiesFrench-language works237,207