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Record W4411333662 · doi:10.1016/j.jcct.2025.04.007

Cardiac computed tomography for prosthetic heart valve assessment. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the European Society of Cardiovascular Radiology (ESCR), the North American Society of Cardiovascular Imaging (NASCI), the Radiological Society of North America (RSNA), the Society for Cardiovascular Angiography & Interventions (SCAI) and Society of Thoracic Surgeons (STS)

2025· article· en· W4411333662 on OpenAlexaff
Ricardo P.J. Budde, M. Faure, Suhny Abbara, Hatem Alkadhi, Paul Cremer, Gudrun Feuchtner, Holly Gonzales, Todd L. Kiefer, Jonathon Leipsic, Koen Nieman, Jonathan Revels, Dee Dee Wang, Eric E. Williamson, Moritz C. Wyler von Ballmoos, Brittany A. Zwischenberger, Rodrigo Salgado

Bibliographic record

VenueJournal of cardiovascular computed tomography · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineComputed tomographyRadiologyPositron Emission Tomography-Computed TomographyCardiologyComputed tomography angiographyMedical physicsInternal medicine

Abstract

fetched live from OpenAlex

Prosthetic heart valve (PHV) dysfunction is increasingly seen due to the increase in the number of PHV that are being implanted worldwide. Cardiac CT imaging has emerged as a valuable tool to assess PHVs and determine the cause of dysfunction. This consensus document first summarizes the available techniques for PHV assessment. Then the use of CT in PHV (dys)function assessment is discussed in detail including consensus statements for correct indications and patient selection for CT assessment of PHVs, image acquisition, reconstruction and measurement protocols and how to interpret and report the CT findings for specific types of PHV dysfunction.

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)0.005
metaresearch head score (Gemma)0.007
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: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.003

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 head0.017
GPT teacher head0.305
Teacher spread0.288 · 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

Citations9
Published2025
Admission routes1
Has abstractno

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