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Record W4245534220 · doi:10.29007/6bnf

Computational Fluid Dynamics Simulations of Flow in the Renal Arteries after Stent Graft Implantation

2018· paratext· en· W4245534220 on OpenAlexaff
Tianyi Xia, Matthew G. Doyle, Thomas L. Forbes, Cristina H. Amon

Bibliographic record

VenueEasyChair preprint · 2018
Typeparatext
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFenestrationHemodynamicsAneurysmBlood flowStentAbdominal aortic aneurysmRenal arteryMedicineBiomedical engineeringMechanicsRadiologyCardiologySurgeryInternal medicinePhysicsKidney

Abstract

fetched live from OpenAlex

The objective of this work is to report a computational fluid dynamics study assessing the hemodynamic effects of fenestration misalignment, towards understanding post-surgical complications of fenestrated endovascular aneurysm repair for abdominal aortic aneurysms. Idealized models were constructed based on geometries from a patient with an infrarenal aortic aneurysm. Fenestrated stent grafts were simulated in the models, with combinations of different fenestration misalignments and takeoff angles. Computational fluid dynamics simulations were performed by solving the governing equations for blood flow under physiologically realistic boundary conditions. Hemodynamic results of renal artery flow rate and time-averaged wall shear stresses were analyzed to build connections between the degree of fenestration misalignment, the takeoff angle, and changes in flow dynamics.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.015
GPT teacher head0.292
Teacher spread0.277 · 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 designSimulation or modeling
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

Citations17
Published2018
Admission routes1
Has abstractyes

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