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Record W4377047062 · doi:10.1093/eurheartjsupp/suad106

ANMCO position paper on vericiguat use in heart failure: from evidence to place in therapy

2023· article· en· W4377047062 on OpenAlexaff
Stefania Angela Di Fusco, Alessandro Alonzo, Alberto Aimo, Andrea Matteucci, Rita Cristina Myriam Intravaia, Stefano Aquilani, Manlio Cipriani, Leonardo De Luca, Alessandro Navazio, Serafina Valente, Michele Massimo Gulizia, Domenico Gabrielli, Fabrizio Oliva, Furio Colivicchi

Bibliographic record

VenueEuropean Heart Journal Supplements · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsMedicineHeart failureGuidelineCyclic guanosine monophosphateIntensive care medicineEjection fractionPosition paperPharmacologyNitric oxideInternal medicinePathology

Abstract

fetched live from OpenAlex

In the growing therapeutic armamentarium for heart failure (HF) management, vericiguat represents an innovative therapeutic option. The biological target of this drug is different from that of other drugs for HF. Indeed, vericiguat does not inhibit neuro-hormonal systems overactivated in HF or sodium-glucose co-transporter 2 but stimulates the biological pathway of nitric oxide and cyclic guanosine monophosphate, which is impaired in patients with HF. Vericiguat has recently been approved by international and national regulatory authorities for the treatment of patients with HF and reduced ejection fraction who are symptomatic despite optimal medical therapy and have worsening HF. This ANMCO position paper summarises key aspects of vericiguat mechanism of action and provides a review of available clinical evidence. Furthermore, this document reports use indications based on international guideline recommendations and local regulatory authority approval at the time of writing.

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)0.007
metaresearch head score (Gemma)0.017
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0020.002
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0210.010

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 head0.086
GPT teacher head0.345
Teacher spread0.258 · 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
GenreCommentary

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

Citations15
Published2023
Admission routes1
Has abstractyes

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