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Record W4407878661 · doi:10.3390/surgeries6010015

Addressing Peri-Device Leaks in Next-Generation Transcatheter Left Atrial Appendage Occluders: An Open Question

2025· article· en· W4407878661 on OpenAlexaff
Majid Roshanfar, Sun-Joo Jang, Albert J. Sinusas, S. Chiu Wong, Bobak Mosadegh

Bibliographic record

VenueSurgeries · 2025
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsConcordia University
FundersNational Institutes of Health
KeywordsAppendageMedicineCardiologyAnatomy

Abstract

fetched live from OpenAlex

With FDA-approved devices, left atrial appendage (LAA) occlusion has emerged as a well-established and rapidly growing approach to stroke prevention in patients with non-valvular atrial fibrillation. These devices are indicated for use in patients who are at increased risk of stroke and systemic embolism, as determined by CHA2DS2-VASc scores, and are suitable for anticoagulation therapy, with an appropriate rationale for seeking a non-pharmacologic alternative. This includes patients who may be unsuitable for long-term anticoagulation due to contra-indications. These devices, generally consisting of a nitinol-framed structure with a circular cross-section, are positioned within the LAA to obstruct the ostium, effectively preventing the thrombus from embolizing the brain. The initial clinical data from pivotal trials and observational registries indicated no strong correlation between peri-device leaks (PDLs) and adverse events. However, recent studies have shown that PDLs are associated with a higher risk of thrombo-embolic events, leading to renewed interest in managing PDLs. This paper reviews the occurrence of PDLs after percutaneous LAA occlusion using current FDA-approved devices, highlighting the need for non-circular occluders to better-accommodate the inherent variability in LAA anatomy. It also compares the benefits and limitations of emerging approaches still under investigation, focusing on addressing PDLs.

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 score0.403
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.278
GPT teacher head0.427
Teacher spread0.149 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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