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Record W3029617361 · doi:10.1002/ejhf.1922

Epidemiology, Pathophysiology and Contemporary Management of Cardiogenic Shock – A Position Statement from the Heart Failure Association of the European Society of Cardiology

2020· review· en· W3029617361 on OpenAlexaff
Ovidiu Chioncel, John Parissis, Alexandre Mebazaa, Hölger Thiele, Steffen Desch, Johann Bauersachs, Veli‐Pekka Harjola, Elena‐Laura Antohi, Mattia Arrigo, Tuvia Ben Gal, Jelena Čelutkienė, Sean P. Collins, Daniel De Backer, Vlad Anton Iliescu, Ewa A. Jankowska, Tiny Jaarsma, Kalliopi Keramida, Mitja Lainščak, Lars H. Lund, Alexander R. Lyon, Josep Masip, Marco Metra, Òscar Miró, Andrea Mortara, Christian Mueller, Wilfried Müllens, Maria Nikolaou, Massimo Piepoli, Susana Price, Giuseppe Rosano, Antoine Vieillard‐Baron, Jean Marc Weinstein, Stefan D. Anker, Gerasimos Filippatos, Frank Ruschitzka, Andrew J.S. Coats, Petar Seferović

Bibliographic record

VenueEuropean Journal of Heart Failure · 2020
Typereview
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsSurgical Specialties (Canada)
FundersRelypsaRespicardiaAbbott VascularServierPfizerVifor PharmaImpulse DynamicsHeartWareBoston Scientific CorporationEisaiSanofiMyoKardiaBristol-Myers SquibbAstraZenecaAmgen
KeywordsMedicineCardiogenic shockIntensive care medicineHeart failureClinical trialPsychological interventionEpidemiologyHarmTranslational researchCardiologyInternal medicinePathologyMyocardial infarctionNursing

Abstract

fetched live from OpenAlex

Cardiogenic shock (CS) is a complex multifactorial clinical syndrome with extremely high mortality, developing as a continuum, and progressing from the initial insult (underlying cause) to the subsequent occurrence of organ failure and death. There is a large spectrum of CS presentations resulting from the interaction between an acute cardiac insult and a patient's underlying cardiac and overall medical condition. Phenotyping patients with CS may have clinical impact on management because classification would support initiation of appropriate therapies. CS management should consider appropriate organization of the health care services, and therapies must be given to the appropriately selected patients, in a timely manner, whilst avoiding iatrogenic harm. Although several consensus-driven algorithms have been proposed, CS management remains challenging and substantial investments in research and development have not yielded proof of efficacy and safety for most of the therapies tested, and outcome in this condition remains poor. Future studies should consider the identification of the new pathophysiological targets, and high-quality translational research should facilitate incorporation of more targeted interventions in clinical research protocols, aimed to improve individual patient outcomes. Designing outcome clinical trials in CS remains particularly challenging in this critical and very costly scenario in cardiology, but information from these trials is imperiously needed to better inform the guidelines and clinical practice. The goal of this review is to summarize the current knowledge concerning the definition, epidemiology, underlying causes, pathophysiology and management of CS based on important lessons from clinical trials and registries, with a focus on improving in-hospital management.

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)0.012
metaresearch head score (Gemma)0.018
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 score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0010.001

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 head0.034
GPT teacher head0.260
Teacher spread0.226 · 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

Citations489
Published2020
Admission routes1
Has abstractyes

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