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Record W3121461358 · doi:10.1186/s42155-021-00208-8

Chronic abdominal aortic dissection, endovascular treatment using a new Stent-graft for in situ Fenestration

2021· article· en· W3121461358 on OpenAlexaff
Hernán Bertoni, Germán Girela, Hector D. Baronè, Federico De Caso, Alejandro de la Vega, Bao T. Bui, Thomas S. Maldonado

Bibliographic record

VenueCVIR Endovascular · 2021
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineFenestrationStentSurgeryAortic dissectionThoracic aortaRadiologyAbdominal aortic aneurysmAbdominal aortaAortic aneurysmDissection (medical)Endovascular treatmentLumen (anatomy)AortaAneurysm

Abstract

fetched live from OpenAlex

BACKGROUND: Although endovascular treatment of the thoracic aorta (TEVAR) has become an elective procedure for treatment of complicated type B aortic dissection, its role in treating post dissection thoraco-abdominal aortic aneurysm (TAAA), is still limited. This is a case of aortic vascular disease, which reports the use of a new endovascular device. CASE PRESENTATION: We present the case of a 62 year old male patient with a history of hypertension, active smoker, who presented penetrating descending thoracic aortic ulcer in the setting of a chronic abdominal aortic dissection. The patient was treated using a new stent graft capable of in situ fenestration that allowed crossing the stent-graft membrane, implanting a covered stent to exclude the re-entry at the level of the left renal artery and redirecting the blood flow through the true lumen. CONCLUSIONS: This case report demonstrates the feasibility of a novel stent-graft concept. Larger studies with longer follow-up are essential to fully evaluate the safety and effectiveness of this new design.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.297
Teacher spread0.254 · 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 designCase report
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

Citations1
Published2021
Admission routes1
Has abstractyes

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