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Record W4318930014 · doi:10.2459/jcm.0000000000001413

Innovations in medical therapy of heart failure with reduced ejection fraction

2022· review· en· W4318930014 on OpenAlexaff
Valentina Mercurio, Giuseppe Ambrosio, Michele Correale, Frank Lloyd Dini, Stefano Ghio, Savina Nodari, Alberto Palazzuoli, Gaetano Ruocco, Roberto Pedrinelli, Giuseppe Mercuro, Pasquale Perrone Filardi, Piergiuseppe Agostoni, Carlo G. Tocchetti, Stefania Paolillo

Bibliographic record

VenueJournal of Cardiovascular Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineHeart failureExacerbationEjection fractionIntensive care medicineDiseaseTherapeutic approachHeart diseasePathologicalCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Heart failure with reduced ejection fraction (HFrEF) is a pathological condition still characterized by high rates of mortality and disease exacerbation frequently leading to hospitalization, thus there is a continuous need for pharmacological treatments impacting on disease stability and long-term prognosis. Moreover, the phenotype of heart failure patients is continuously changing over time, and the development of new heart failure drugs is crucial to promote a personalized and targeted approach. In recent years, several therapeutic innovations have emerged in the landscape of acute and chronic HFrEF, largely changing and improving our approach to the disease. Various studies on new drugs and experimental therapeutic approaches are ongoing. The present review discusses the latest data on both recently approved drugs and developing therapeutic targets, in order to provide a critical overview for an informed and optimal approach to such a complex disease.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.338
Teacher spread0.292 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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