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Record W4317797124 · doi:10.1016/j.jacc.2022.11.023

Worsening Heart Failure: Nomenclature, Epidemiology, and Future Directions

2023· review· en· W4317797124 on OpenAlexaff
Stephen J. Greene, Johann Bauersachs, Jasper J. Brugts, Justin A. Ezekowitz, Carolyn S.P. Lam, Lars H. Lund, Piotr Ponikowski, Adriaan A. Voors, Faı̈ez Zannad, Shelley Zieroth, Javed Butler

Bibliographic record

VenueJournal of the American College of Cardiology · 2023
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of ManitobaCanadian VIGOUR CentreUniversity of Alberta
FundersBayer
KeywordsMedicineHeart failureIntensive care medicineClinical trialEpidemiologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Heart failure (HF) is a progressive disease characterized by variable durations of symptomatic stability often punctuated by episodes of worsening despite continued therapy. These periods of clinical worsening are increasingly recognized as a distinct phase in the history of HF, termed worsening HF (WHF). The definition of WHF continues to evolve from a historical focus solely on hospitalization to now include nonhospitalization events (eg, need for intravenous diuretic therapy in the emergency or outpatient setting). Most HF clinical trials to date have had HF hospitalization and death as primary endpoints, and only recently, some studies have included other WHF events regardless of location of care. This article reviews the evolution of the WHF definition, highlights the importance of considering the onset of WHF as an event that marks a new phase of HF, summarizes the latest clinical trials investigating novel therapies, and outlines unmet needs regarding identification and treatment of WHF.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0060.006
Science and technology studies0.0000.002
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.046
GPT teacher head0.354
Teacher spread0.308 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations273
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American College of CardiologySame topicHeart Failure Treatment and ManagementFrench-language works237,207