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Record W4387335043 · doi:10.3389/fcvm.2023.1255503

Tricuspid regurgitation, right ventricular function, and renal congestion: a cardiorenal triangle

2023· review· en· W4387335043 on OpenAlexaff
Ilana Forado-Benatar, Pedro Caravaca Pérez, Diana Rodríguez‐Espinosa, Joan Guzman-Bofarull, Elena Cuadrado‐Payán, Yasbanoo Moayedi, José Jesús Broseta, Marta Farrero

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsTed Rogers Centre for Heart ResearchUniversity Health Network
Fundersnot available
KeywordsMedicineRegurgitation (circulation)CardiologyCardiorenal syndromeIntensive care medicineInternal medicinevalvular heart diseaseComplicationPercutaneousHepatorenal syndromeHeart failure

Abstract

fetched live from OpenAlex

There is a growing interest in the evaluation of tricuspid regurgitation due to its increasing prevalence and detrimental impact on clinical outcomes. Historically, it has been coined the "forgotten" defect in the field of valvular heart disease due to the lack of effective treatments to improve prognosis. However, the development of percutaneous treatment techniques has led to a new era in its management, with promising results and diminished complication risk. In spite of these advances, a comprehensive exploration of the pathophysiological mechanisms is essential to establish clear indications and optimal timing for medical and percutaneous intervention. This review will address the most important aspects related to the diagnosis, pathophysiology and treatment of tricuspid regurgitation from a cardiorenal perspective, with a special emphasis on the interaction between right ventricular dysfunction and the development of hepatorenal congestion.

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)0.000
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.325
Teacher spread0.297 · 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
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

Citations15
Published2023
Admission routes1
Has abstractyes

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