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Record W2494740518 · doi:10.1016/j.ejvssr.2016.06.002

Challenges of “Reverse” Aortic Arch Debranching for Repair of the Ascending Aorta by Thoracic Endovascular Aortic Repair

2016· article· en· W2494740518 on OpenAlexaff
Vinay Kansal, Adnan Hadziomerovic, Sudhir Nagpal

Bibliographic record

VenueEJVES Short Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineAscending aortaAortic repairAortic archThoracic aortic aneurysmCardiologyInternal medicineAortic aneurysmAorta

Abstract

fetched live from OpenAlex

INTRODUCTION: Ascending aortic pathology presents a unique challenge for treatment by thoracic endovascular aortic repair (TEVAR), because of lack of adequate endograft landing zones. This report describes a unique "reverse" extra-anatomical aortic arch debranching procedure performed to enable TEVAR of the ascending aorta. REPORT: A 71-year-old male presented with a large ascending aortic pseudoaneurysm secondary to previous arch repair anastomosis. This pathology was treated by TEVAR of the ascending aorta. To create a sufficient landing zone for the endovascular stent graft, a "reverse" extra-anatomical aortic arch debranching procedure was performed. This involved a left subclavian artery to left carotid artery bypass, left to right carotid-to-carotid bypass, ligation of proximal left common carotid artery, and embolization of the innominate artery origin. DISCUSSION: TEVAR of the ascending aorta can be made feasible through a novel debranching procedure that creates sufficient landing zones for the endograft. This surgical approach may prove useful in patients who present with aortic arch pathology and comorbidities that prevent open surgical repair.

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)0.001
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 score0.247
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.040
GPT teacher head0.314
Teacher spread0.274 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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