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Record W3119899557

Hemodynamics Assessments of Ascending Thoracic Aortic Aneurysm - the Influence of Hematocrit with Fluid-Structure Interaction Analysis

2019· article· en· W3119899557 on OpenAlexaff
Han Hung Yeh, Simon W. Rabkin, Dana Grecov

Bibliographic record

VenueAPS Division of Fluid Dynamics Meeting Abstracts · 2019
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHematocritMedicineCardiologyHemodynamicsInternal medicineAneurysmBlood flowAortic aneurysmShear stressThoracic aortic aneurysmAortaRadiologyMechanicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

Aortic aneurysm is one of the cardiovascular diseaseswith localized abnormal growth of a blood vessel with a risk of rupture or dissect. The precise pathological pathway for disease progression in aneurysm formation is not completely understood. In the current study, ascending thoracic aortic aneurysms are investigated using fully coupled fluid-structure interactionmethod with the focus to investigate the importance of changes in hematocrit under normotension and hypertension. Blood was modelled as incompressible flow within laminar regimewith the use of the Quemada model to account for the effect of hematocrits. The anisotropic hyperelastic properties of the aortic wall were considered. Given the change in the degreeof shear thinning from the non-Newtonian behavior of blood due to the change in hematocrit, the simulated result could provide valuable information in clinical practice. Indeed, our results suggested that with the increase in hematocrit, the shear stress distribution as well as the maximum shear stress magnitude along the arterial wall would increase significantly. The arterial wall stress distributions, however, remained unchanged with respect to the changes in hematocrit.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.312
Teacher spread0.303 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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