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Record W2999963305 · doi:10.1097/hco.0000000000000711

Modes of bioprosthetic valve failure: a narrative review

2020· review· en· W2999963305 on OpenAlexaff
Alex Koziarz, Ahmad Makhdoum, Jagdish Butany, Maral Ouzounian, Jennifer Chung

Bibliographic record

VenueCurrent Opinion in Cardiology · 2020
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsPannusDegeneration (medical)MedicineCalcificationCardiologyInternal medicineImplantValve replacementEndocarditisSurgeryStenosisPathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: A thorough understanding of the modes of bioprosthetic valve failure is critical as clinicians will be facing an increasing number of patients presenting with failed bioprostheses in coming years. The purpose of this article is to review modes of bioprosthestic valve degeneration, their management, and identify gaps for future research. RECENT FINDINGS: Guidelines recommend monitoring hemodynamic performance of prosthetic valves using serial echocardiograms to determine valve function and presence of valve degeneration. Modes of bioprosthetic valve failure may be categorized as structural degeneration (calcification, tears, fibrosis, flail), nonstructural degeneration (pannus), thrombosis, and endocarditis. Calcification is the most common form of structural valve degeneration. Predictors of bioprosthetic valve failure include valves implanted in the mitral position, younger age, and type of valve (porcine versus bovine pericardial). Failed bioprosthetic valves are managed with either redo surgical replacement or transcatheter valve-in-valve implantation. SUMMARY: Several modes of bioprosthetic valve failure exist, which vary based on patient, implant position, and valve characteristics. Further research is required to characterize factors associated with early failure to delay structural valve degeneration and improve patient prognosis.

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 score0.848
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.006
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 head0.099
GPT teacher head0.473
Teacher spread0.374 · 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

Citations58
Published2020
Admission routes1
Has abstractyes

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