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Record W4410510778 · doi:10.4244/eij-d-24-01126

SCAI/EAPCI/ACVC Expert Consensus Statement on Cardiogenic Shock in Women

2025· article· en· W4410510778 on OpenAlexaff
Suzanne J. Baron, Josephine Chou, Tayyab Shah, Amanda R. Vest, J. Dawn Abbott, Mirvat Alasnag, Cristina Aurigemma, Emanuele Barbato, Lavanya Bellumkonda, Anna E. Bortnick, Alaide Chieffo, Robert-Jan van Geuns, Cindy L. Grines, Sigrun Halvorsen, Christian Hassager, Navin K. Kapur, Srihari S. Naidu, Vivian G. Ng, Jacqueline Saw, Alexandra J. Lansky

Bibliographic record

VenueEuroIntervention · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Issues in Pregnancy
Canadian institutionsVancouver General Hospital
FundersRadboud Universitair Medisch CentrumUniversitetet i OsloSapienza Università di RomaRadboud UniversiteitRigshospitaletBrown UniversityTufts Medical CenterYale UniversityCleveland ClinicUniversity of PennsylvaniaMassachusetts General Hospital
KeywordsMedicineCardiogenic shockIntensive care medicineStatement (logic)DiseaseBest practiceCardiovascular healthShock (circulatory)Family medicineCardiologyInternal medicineMyocardial infarctionLaw

Abstract

fetched live from OpenAlex

Cardiovascular disease is the leading cause of death for women worldwide, with mortality rates due to cardiogenic shock (CS) remaining exceedingly high. Sex-based disparities in the timely delivery of optimal CS treatment contribute to poor outcomes; addressing these disparities is a major priority to improve women's cardiovascular health. This consensus statement provides a comprehensive summary of the current state of treatment of CS in women across the spectrum of cardiovascular disease states and identifies important gaps in evidence. As sex-based data are limited in contemporary literature, clinicians may use this document as a resource to guide practice. Further investigations are necessary to inform best practices for the diagnosis and treatment of women with CS.

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)0.019
metaresearch head score (Gemma)0.046
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: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.046
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0040.002
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0050.003
Research integrity0.0100.010
Insufficient payload (model declined to judge)0.0090.007

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 head0.019
GPT teacher head0.323
Teacher spread0.304 · 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
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

Citations5
Published2025
Admission routes1
Has abstractyes

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