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Record W4411844219 · doi:10.1177/15569845251347968

State-of-the-Art Review of Aortic Arch Reconstruction With the Frozen Elephant Trunk

2025· review· en· W4411844219 on OpenAlexaff
Sabin J. Bozso, Ryaan EL‐Andari, Rashmi Nedadur, Brandon Loshusan, Holly N. Smith, Jennifer Chung, Jonathan Hong, François Dagenais, Marina Ibrahim, Michael C. Moon, Michael Chu

Bibliographic record

VenueInnovations Technology and Techniques in Cardiothoracic and Vascular Surgery · 2025
Typereview
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversity of TorontoMontreal Heart InstituteUniversity of ManitobaWestern UniversityUniversity of CalgaryUniversité LavalUniversity of Alberta
Fundersnot available
KeywordsElephant trunksMedicineAortic archArchAortic dissectionStentSurgeryRadiologyAorta

Abstract

fetched live from OpenAlex

Aortic arch replacement operations have undergone substantial evolution with technical advancements, notably the introduction of the frozen elephant trunk (FET) technique. The purpose of this state-of-the-art review is to detail our approach to contemporary aortic arch replacement with FET operations. First, we review the evolution of FET procedures over the years and discuss technical modifications, including cerebral perfusion options, to the aortic arch replacement with FET. We also discuss state-of-the-art technical considerations of head vessel reconstruction and management of the difficult left subclavian artery. We also discuss selected considerations related to the endovascular stent graft component, including landing zone management and when to consider extended distal aortic interventions. We briefly discuss potential complications of which the vigilant clinician should be aware, as well as highlight subtleties in managing aortic dissection compared with aortic aneurysms.

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)0.001
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.319
Teacher spread0.297 · 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
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

Citations3
Published2025
Admission routes1
Has abstractyes

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