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Record W4388837294 · doi:10.4244/eij-d-23-00823

Outcomes and performance of the ACURATE neo2 transcatheter heart valve in clinical practice: one-yearresults of the ACURATE neo2 PMCF Study

2023· article· en· W4388837294 on OpenAlexafffund
Won‐Keun Kim, Helge Möllmann, Matteo Montorfano, Julia Ellert‐Gregersen, Tanja K. Rudolph, Nicolas M. Van Mieghem, Michael Hilker, Ignacio J. Amat‐Santos, Christian Juhl Terkelsen, Anna Sonia Petronio, Pieter R. Stella, Matthias Götberg, Andreas Rück, Albert Markus Kasel, Ramiro Trillo, Clare Appleby, Marco Barbanti, Philipp Blanke, Federico M. Asch, Rodrigo Modolo, Dominic J. Allocco, Corrado Tamburino

Bibliographic record

VenueEuroIntervention · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersPrincess Margaret Cancer FoundationBoston Scientific Corporation
KeywordsMedicineHeart valveCardiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Transcatheter aortic valve implantation is an effective treatment for patients with aortic stenosis; however, complications related to paravalvular leakage (PVL) persist, including increased risk of mortality, cardiovascular mortality, and rehospitalisation. AIMS: We sought to evaluate the clinical outcomes and valve performance at 1 year in patients with severe aortic stenosis treated with the ACURATE neo2 valve in a post-market clinical setting. METHODS: Valve Academic Research Consortium-2 safety events were assessed up to 1 year. Independent core laboratories evaluated echocardiographic measures of valve performance and hypoattenuated leaflet thickening (HALT; as measured by four-dimensional computed tomography). RESULTS: at 1 year). At 1 year, 99% of patients had mild or no/trace PVL (<1% had moderate PVL; no patient had severe PVL). CONCLUSIONS: The study outcomes confirm favourable performance and safety up to 1 year in patients treated with ACURATE neo2 in routine clinical practice. (ClinicalTrials.gov: NCT04655248).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
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 head0.054
GPT teacher head0.426
Teacher spread0.372 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations15
Published2023
Admission routes2
Has abstractyes

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