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Tricuspid Valve Academic Research Consortium Definitions for Tricuspid Regurgitation and Trial Endpoints

2023· review· en· W4387330459 on OpenAlexaff
Rebecca T. Hahn, Matthew Lawlor, Charles J. Davidson, Vinay Badhwar, Anna Sannino, Ernest Spitzer, Philipp Lurz, Brian R. Lindman, Yan Topilsky, Suzanne J. Baron, Scott Chadderdon, Omar Khalique, Gilbert H.L. Tang, Maurizio Taramasso, Paul Grayburn, Luigi P. Badano, Jonathon Leipsic, JoAnn Lindenfeld, Stephan Windecker, Sreekanth Vemulapalli, Björn Redfors, Maria Alu, David J. Cohen, Josep Rodés‐Cabau, Gorav Ailawadi, Michael J. Mack, Ori Ben‐Yehuda, Martin B. Leon, Jörg Hausleiter, Suzanne V. Arnold, Vinayak Bapat, Natalia Berry, Philipp Blanke, Daniel Burkhoff, Megan Coylewright, Neal Duggal, Benjamin Z. Galper, Isaac George, Mayra Guerrero, Nadira Hamid, Vikrant Jagadeesan, Susheel Kodali, Mitch Krucoff, Roberto M. Lang, Mahesh V. Madhavan, Vallerie McLaughin, Roxana Mehran, François Philippon, Sanjum S. Sethi, Matheus Simonato, Robert L. Smith, Nishtha Sodhi, John A. Spertus, Thomas J. Stocker, Gregg W. Stone

Bibliographic record

VenueThe Annals of Thoracic Surgery · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalUniversity of British Columbia
Fundersnot available
KeywordsMedicineRegurgitation (circulation)Tricuspid valveCardiologyTricuspid Valve InsufficiencyInternal medicine

Abstract

fetched live from OpenAlex

Interest in the pathophysiology, etiology, management, and outcomes of patients with tricuspid regurgitation (TR) has grown in the wake of multiple natural history studies showing progressively worse outcomes associated with increasing TR severity, even after adjusting for multiple comorbidities. Historically, isolated tricuspid valve surgery has been associated with high in-hospital mortality rates, leading to the development of transcatheter treatment options. The aim of this first Tricuspid Valve Academic Research Consortium document is to standardize definitions of disease etiology and severity, as well as endpoints for trials that aim to address the gaps in our knowledge related to identification and management of patients with TR. Standardizing endpoints for trials should provide consistency and enable meaningful comparisons between clinical trials. A second Tricuspid Valve Academic Research Consortium document will focus on further defining trial endpoints and will discuss trial design options.

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)0.331
metaresearch head score (Gemma)0.418
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.331
Threshold uncertainty score0.825

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3310.418
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0050.010
Bibliometrics0.0070.012
Science and technology studies0.0030.004
Scholarly communication0.0170.006
Open science0.0070.011
Research integrity0.0100.014
Insufficient payload (model declined to judge)0.0100.006

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 head0.715
GPT teacher head0.617
Teacher spread0.098 · 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.

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

Citations25
Published2023
Admission routes1
Has abstractyes

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