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Record W4399368175 · doi:10.1016/j.jvsvi.2024.100098

Endovascular management options and techniques for ruptured thoracoabdominal aortic aneurysm

2024· article· en· W4399368175 on OpenAlexaff
Ming Hao Guo, Thomas Le Houérou, Antoine Gaudin, Alessandro Costanzo, Dominique Fabre, Stéphan Haulon

Bibliographic record

VenueJVS-Vascular Insights · 2024
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineAortic aneurysmAneurysmAortic ruptureEndovascular treatmentRadiologyCardiologyInternal medicine

Abstract

fetched live from OpenAlex

ObjectiveOpen surgical repair of ruptured thoracoabdominal aortic aneurysm (rTAAA) carries significant risk of mortality and morbidity; in the recent years, endovascular repair has emerged as a suitable alternative. This article aims to review current available technologies, techniques, and outcomes for endovascular repair of rTAAA.MethodsA narrative review of current literature was performed.ResultsOff-the-shelf branched endografts are available and are often the first-line endovascular therapy for type I-III rTAAA or type IV rTAAA with a lumen diameter ≥ 24 mm at the level of the reno-visceral vessels. In patients with unsuitable anatomy for off-the-shelf branch devices, particularly those with ruptured type IV or pararenal TAAA with narrow aortic lumen, endovascular repair with in-situ laser fenestration is a reasonable alternative. Physician-modified devices as well as endovascular repair with parallel stent grafts (chimney, periscope, sandwich, or Octopus) has been described by select centers with satisfactory outcomes.ConclusionsPatients with rTAAA and suitable anatomy who are at high- or prohibitive-surgical risk can be managed endovascularly with comparative outcomes. Various techniques are described in the literature, and the choices of technique used should depend on patient anatomy and surgeon expertise.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.284
Teacher spread0.266 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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