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Record W3142478671 · doi:10.1093/eurheartj/ehab196

Management of antithrombotic therapy in patients undergoing transcatheter aortic valve implantation: a consensus document of the ESC Working Group on Thrombosis and the European Association of Percutaneous Cardiovascular Interventions (EAPCI), in collaboration with the ESC Council on Valvular Heart Disease

2021· article· en· W3142478671 on OpenAlexaff
Jurriën M. ten Berg, Dirk Sibbing, Bianca Rocca, Éric Van Belle, Bernard Chevalier, Jean‐Philippe Collet, Dariusz Dudek, Martine Gilard, Diana A. Gorog, Julia Grapsa, Erik Lerkevang Grove, Patrizio Lancellotti, Anna Sonia Petronio, Andrea Rubboli, Lucia Torracca, Gemma Vilahur, Adam Witkowski, Julinda Mehilli

Bibliographic record

VenueEuropean Heart Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsSt. Thomas Hospital
FundersDaiichi Sankyo EuropeBoston Scientific CorporationPfizerBristol-Myers Squibb
KeywordsMedicineAntithromboticPercutaneousCardiologyInternal medicineThrombosisConsensus conferencePsychological interventionPercutaneous coronary interventionSurgeryMyocardial infarction

Abstract

fetched live from OpenAlex

Transcatheter aortic valve implantation (TAVI) is effective in older patients with symptomatic severe aortic stenosis, while the indication has recently broadened to younger patients at lower risk. Although thromboembolic and bleeding complications after TAVI have decreased over time, such adverse events are still common. The recommendations of the latest 2017 ESC/EACTS Guidelines for the management of valvular heart disease on antithrombotic therapy in patients undergoing TAVI are mostly based on expert opinion. Based on recent studies and randomized controlled trials, this viewpoint document provides updated therapeutic insights in antithrombotic treatment during and after TAVI.

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)0.009
metaresearch head score (Gemma)0.013
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: Methods · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.293
Teacher spread0.260 · 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
GenreMethods

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

Citations142
Published2021
Admission routes1
Has abstractyes

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