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Record W2606485132 · doi:10.1093/eurheartj/ehx211

Clinical Trial Principles and Endpoint Definitions for Paravalvular Leaks in Surgical Prosthesis

2017· article· en· W2606485132 on OpenAlexaff
Carlos E. Ruiz, Rebecca T. Hahn, Alain Berrébi, Jeffrey Borer, Donald E. Cutlip, Greg Fontana, Gino Gerosa, Réda Ibrahim, Vladimir Jelnin, Hasan Jilaihawi, Chad Kliger, Itzhak Kronzon, Jonathon Leipsic, Francesco Maisano, Xavier Millán, Patrick Nataf, Patrick T. O’Gara, Philippe Pîbarot, Stephen R. Ramee, Charanjit S. Rihal, Josep Rodés‐Cabau, Paul Sorajja, Rakesh M. Suri, Julie A. Swain, Zoltan G. Turi, E. Murat Tuzcu, Neil J. Weissman, José Luis Zamorano, Patrick W. Serruys, Martin B. Leon

Bibliographic record

VenueEuropean Heart Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of British ColumbiaSt. Paul's HospitalMontreal Heart Institute
Fundersnot available
KeywordsMedicineConcordanceClinical endpointClinical trialEndpoint DeterminationGrading (engineering)Intensive care medicineValve replacementSurgeryMedical physicsInternal medicine

Abstract

fetched live from OpenAlex

The VARC (Valve Academic Research Consortium) for transcatheter aortic valve replacement set the standard for selecting appropriate clinical endpoints reflecting safety and effectiveness of transcatheter devices, and defining single and composite clinical endpoints for clinical trials. No such standardization exists for circumferentially sutured surgical valve paravalvular leak (PVL) closure. This document seeks to provide core principles, appropriate clinical endpoints, and endpoint definitions to be used in clinical trials of PVL closure devices. The PVL Academic Research Consortium met to review evidence and make recommendations for assessment of disease severity, data collection, and updated endpoint definitions. A 5-class grading scheme to evaluate PVL was developed in concordance with VARC recommendations. Unresolved issues in the field are outlined. The current PVL Academic Research Consortium provides recommendations for assessment of disease severity, data collection, and endpoint definitions. Future research in the field is warranted.

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)0.448
metaresearch head score (Gemma)0.355
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.552
Threshold uncertainty score0.681

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4480.355
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.009
Bibliometrics0.0070.007
Science and technology studies0.0040.007
Scholarly communication0.0100.004
Open science0.0050.005
Research integrity0.0100.017
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 head0.297
GPT teacher head0.476
Teacher spread0.179 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
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

Citations46
Published2017
Admission routes1
Has abstractyes

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