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Record W4401522738 · doi:10.4244/eij-d-23-01084

Antithrombotic therapy for transcatheter structural heart intervention

2024· review· en· W4401522738 on OpenAlexafffund
Paul Guedeney, Josep Rodés‐Cabau, Jurriën M. ten Berg, Stephan Windecker, Dominick J. Angiolillo, Gilles Montalescot, Jean‐Philippe Collet

Bibliographic record

VenueEuroIntervention · 2024
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité Laval
FundersCARIM School for Cardiovascular Diseases, Universiteit MaastrichtInstitut National de la Santé et de la Recherche MédicaleInselspital, Universitätsspital BernSt. Antonius ZiekenhuisUniversity of BernSorbonne UniversitéUniversité Laval
KeywordsMedicinePatent foramen ovaleAntithromboticPsychological interventionPercutaneousIntensive care medicineCardiologyGuidelineHeart valveInternal medicineSurgery

Abstract

fetched live from OpenAlex

Percutaneous transcatheter structural heart interventions have considerably expanded within the last two decades, improving clinical outcomes and quality of life versus guideline-directed medical therapy for patients frequently ineligible for surgical treatment. Transcatheter structural heart interventions comprise valve implantation or repair and also occlusions of the patent foramen ovale, atrial septal defects and left atrial appendage. These procedures expose structural devices to arterial or venous blood flow with various rheological conditions leading to potential thrombotic complications and embolisation. Furthermore, these procedures may concern comorbid patients at high risk of both ischaemic and bleeding complications. This state-of-the-art review provides a description of the device-related thrombotic risk associated with these transcatheter structural heart interventions and of the current evidence-based guidelines regarding antithrombotic treatments. Gaps in evidence for each of the studied transcatheter interventions and the main ongoing trials are also summarised.

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), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.023
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 head0.075
GPT teacher head0.471
Teacher spread0.396 · 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 designOther design
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

Citations9
Published2024
Admission routes2
Has abstractyes

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