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Record W2905833275 · doi:10.1097/hco.0000000000000602

Early failure of aortic bioprostheses

2018· review· en· W2905833275 on OpenAlexaff
Amine Mazine, Subodh Verma, Bobby Yanagawa

Bibliographic record

VenueCurrent Opinion in Cardiology · 2018
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineCardiologyInternal medicineAortic valveHemodynamicsSubclinical infectionRegurgitation (circulation)Aortic valve replacementStenosis

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The purpose of this study is to review the contemporary evidence surrounding aortic bioprosthetic valve deterioration, with a focus on early failure of surgically implanted valves. RECENT FINDINGS: Structural valve deterioration (SVD) remains the most frequent cause of premature bioprosthetic aortic valve failure. However, recent evidence suggests that SVD represents a spectrum, and that clinically silent hemodynamic valve deterioration frequently precedes and predisposes to overt SVD. Hemodynamic valve deterioration is defined as an increase in mean transprosthetic gradient and/or worsening transprosthetic regurgitation on echocardiography. Novel evidence suggests that a dysmetabolic profile may predispose to this phenomenon. Furthermore, subclinical leaflet thrombosis is increasingly recognized as a potential cause of hemodynamic deterioration of bioprosthetic valves. Collectively, these findings highlight the importance of systematic and regular imaging surveillance following bioprosthetic aortic valve replacement. SUMMARY: Early failure of bioprosthetic aortic valves is a complex and multifactorial phenomenon. Further studies are needed to determine the optimal timing and imaging modality for surveillance following bioprosthetic aortic valve replacement, and to establish strategies to prevent and treat aortic bioprosthetic failure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.108
GPT teacher head0.464
Teacher spread0.357 · 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 teacher head, 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

Citations6
Published2018
Admission routes1
Has abstractyes

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