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Record W2901282573 · doi:10.1055/s-0038-1673670

Diagnosis and Management of Acquired von Willebrand Disease in Heart Disease: A Review of the Literature

2018· review· en· W2901282573 on OpenAlexaff
Mate Petričević, J Knezević, Gordan Samoukovic, Božena Bradarić, Ivica Šafradin, Marija Meštrović, Vasil Papestiev, Alen Hodalin, Tomislav Madžar, Mario Mihalj, Ante Rotim, Bojan Biočina

Bibliographic record

VenueThe Thoracic and Cardiovascular Surgeon · 2018
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineVon Willebrand factorDiseaseHeart diseaseVon Willebrand diseaseOccultADAMTS13CardiologyInternal medicineIntensive care medicinePathologyPlatelet

Abstract

fetched live from OpenAlex

The incidence of acquired von Willebrand syndrome (AvWS) in patients with heart disease is commonly perceived as rare. However, its occurrence is underestimated and underdiagnosed, potentially leading to inadequate treatment resulting in increased morbidity and mortality.In patients with cardiac disease, AvWS frequently occurs in patients with structural heart disease and in those undergoing mechanical circulatory support (MCS).The clinical manifestation of an AvWS is usually characterized by apparent or occult gastrointestinal (GI) or mucocutaneous hemorrhage frequently accompanied by signs of anemia and/or increased bleeding during surgical procedures. The primary change is loss of high-molecular weight von Willebrand factor multimers (HMWM). Whereas the loss of HMWM in patients with structural heart disease is caused by increased HMWM cleavage by von Willebrand factor (vWF)-cleaving protease, ADAMTS13, AvWS in MCS patients is predominantly a result of a high shear stress coupled with mechanical destruction of vWF itself.This manuscript provides a comprehensive review of the evidence regarding both diagnosis and contemporary management of AVWS in patients with heart disease.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.634
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.302
Teacher spread0.281 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations11
Published2018
Admission routes1
Has abstractyes

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