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Record W3084942051 · doi:10.1093/eurheartj/ehaa542

Leaflet immobility and thrombosis in transcatheter aortic valve replacement

2020· review· en· W3084942051 on OpenAlexaff
Arnold C.T. Ng, David R. Holmes, Michael J. Mack, Victoria Delgado, Raj Makkar, Philipp Blanke, Jonathon Leipsic, Martin B. Leon, Jeroen J. Bax

Bibliographic record

VenueEuropean Heart Journal · 2020
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineThrombusThrombosisCardiologyValve replacementThienopyridineAntithromboticInternal medicineAspirinHeart valveSurgeryRadiologyStenosisClopidogrel

Abstract

fetched live from OpenAlex

Transcatheter aortic valve replacement (TAVR) has grown exponentially worldwide in the last decade. Due to the higher bleeding risks associated with oral anticoagulation and in patients undergoing TAVR, antiplatelet therapy is currently considered first-line antithrombotic treatment after TAVR. Recent studies suggest that some patients can develop subclinical transcatheter heart valve (THV) thrombosis after the procedure, whereby thrombus forms on the leaflets that can be a precursor to leaflet dysfunction. Compared with echocardiography, multidetector computed tomography is more sensitive at detecting THV thrombosis. Transcatheter heart valve thrombosis can occur while on dual antiplatelet therapy with aspirin and thienopyridine but significantly less with anticoagulation. This review summarizes the incidence and diagnostic criteria for THV thrombosis and discusses the pathophysiological mechanisms that may lead to thrombus formation, its natural history, potential clinical implications and treatment for these patients.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.084
GPT teacher head0.413
Teacher spread0.328 · 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

Citations51
Published2020
Admission routes1
Has abstractyes

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