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Record W4408244431 · doi:10.1093/ehjci/jeaf078

Imaging assessment after percutaneous left atrial appendage closure: from immediate to long-term follow-up

2025· review· en· W4408244431 on OpenAlexaff
Pedro Cepas‐Guillén, David R. Holmes, João L. Cavalcante, Gilles O’Hara, Jonathan Beaudoin, Julio I. Farjat‐Pasos, Benoît Labbé, Josep Rodés‐Cabau, Erwan Salaün

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Imaging · 2025
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersBoston Scientific JapanFundación Alfonso Martín EscuderoAbbott Fund
KeywordsPercutaneousMedicineAtrial fibrillationModality (human–computer interaction)ComplicationClosure (psychology)RadiologyModalitiesSurgeryCardiologyComputer science

Abstract

fetched live from OpenAlex

Growing evidence has demonstrated the clinical benefit of percutaneous left atrial appendage closure (LAAC) in patients with atrial fibrillation. Although procedural complication rates have declined with increasing experience, post-procedural device-related complications persist, impacting prognosis and reducing the long-term benefits of the procedure. Given the potential impact of these complications, surveillance imaging after LAAC is mandatory. Currently, different imaging modalities offer unique advantages to manage these complications which warrant a combined approach to optimize both short- and long-term follow-up. The aims of this review are to explore the distinct characteristics of each imaging modality, highlighting the primary findings to be assessed during follow-up imaging. Additionally, we propose an optimized clinical imaging surveillance roadmap from discharge to long-term follow-up.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.014
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.051
GPT teacher head0.360
Teacher spread0.309 · 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 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

Citations8
Published2025
Admission routes1
Has abstractyes

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