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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.394
Threshold uncertainty score1.000

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)0.0010.001

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 head0.015
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