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Record W4406897124 · doi:10.1093/ehjci/jeaf026

The role of multi-modality imaging in multiple valvular heart diseases: a clinical consensus statement of the European Association of Cardiovascular Imaging of the European Society of Cardiology

2025· article· en· W4406897124 on OpenAlexaff
Erwan Donal, Philippe Unger, Augustin Coisne, Philippe Pîbarot, Julien Magné, Marta Sitges, Gilbert Habib, Marie‐Annick Clavel, Sven Plein, Théo Pezel, Marc R. Dweck, Pepe Zamorano, Jordi S. Dahl, Bernard Cosyns, Nina Ajmone Marsan

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Imaging · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersMedical Research CouncilEdwards Lifesciences
KeywordsMedicinevalvular heart diseaseMultimodalityPositron emission tomographyRisk stratificationMagnetic resonance imagingCardiologyCardiac imagingIntensive care medicineMedical physicsInternal medicineRadiology

Abstract

fetched live from OpenAlex

With this document, the European Association of Cardiovascular Imaging provides an Expert Consensus on the role of multi-modality imaging (MMI) in the management of patients with multiple valvular heart disease (MVD). Emphasis is given to the use of MMI to unravel the diagnostic challenges that characterize these patients and to improve risk stratification. Complementing the last European Society of Cardiology and European Association of Cardio-Thoracic Surgery guidelines on valvular heart disease, this Expert Consensus document also outlines how MMI assessment should form an integral part of the multi-disciplinary heart team discussion for patients with MVD to help with complex decision-making regarding the choice and timing of treatment.

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)0.036
metaresearch head score (Gemma)0.037
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: Methods · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0010.002

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 head0.023
GPT teacher head0.329
Teacher spread0.307 · 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
GenreMethods

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

Citations20
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Heart Journal - Cardiovascular ImagingSame topicCardiac Valve Diseases and TreatmentsFrench-language works237,207