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Record W4322500709 · doi:10.3390/medicina59030462

Three-Year Patency Results following Endovascular Transvenous Femoropopliteal Bypass

2023· article· en· W4322500709 on OpenAlexaff
Roberts Rumba, Dainis Krieviņš, Jānis Šavlovskis, Natālija Ezīte, Aigars Lācis, Eva Petrošina, Ludovic Mouttet, Jānis Gardovskis, Christopher K. Zarins

Bibliographic record

VenueMedicina · 2023
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineRevascularizationSurgeryRadiologyPopliteal arterySuperficial femoral arteryProspective cohort studyBypass operationFemoral arteryVeinArteryCardiology

Abstract

fetched live from OpenAlex

Background and Objectives: Peripheral artery disease is one of the most common vascular pathologies. There is an ongoing debate among specialists on whether open or endovascular revascularization is preferred in cases of complex superficial femoral artery (SFA) lesions. The purpose of this study was to assess patency results of a relatively new transvenous endovascular bypass device. This could add to existing evidence and aid in comparison between open and endovascular bypass. Materials and Methods: Patients with complex TASC-C and D SFA lesions who had indications for revascularization were identified. Prospective analysis of stent graft patency from 54 transvenous femoropopliteal bypass procedures was performed. Patency was assessed by Duplex ultrasound every six months. Kaplan–Meier analysis was performed to assess primary, primary-assisted, and secondary patency of transvenous bypass. Results: Following endovascular transvenous femoropopliteal bypass, 3-year graft primary, primary-assisted, and secondary patency was 43.8%, 66.3%, and 73.9%, respectively. Conclusions: Transvenous endovascular femoropopliteal bypass is a viable option for selected patients who lack adequate saphenous vein or have comorbidities that increase the risk of open femoropopliteal bypass. Strict post-operative follow-up is necessary to improve patency rates.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.268
Teacher spread0.245 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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