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Record W3090303265 · doi:10.1115/1.4048570

Blood Flow Manipulation in the Aorta With Coarctation and Arch Narrowing for Pediatric Subjects

2020· article· en· W3090303265 on OpenAlexaff
Yuxi Jia, Kumaradevan Punithakumar, Michelle Noga, Arman Hemmati

Bibliographic record

VenueJournal of Applied Mechanics · 2020
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAortaAortic archBlood flowShear stressCoarctation of the aortaAnatomyMedicineFlow (mathematics)MechanicsPressure dropArchCardiologyMaterials scienceGeologyInternal medicineStructural engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract The characteristics of blood flow in an abnormal pediatric aorta with an aortic coarctation and aortic arch narrowing are examined using direct numerical simulations and patient-specific boundary conditions. The blood flow simulations of a normal pediatric aorta are used for comparison to identify unique flow features resulting from the aorta geometrical anomalies. Despite flow similarities compared to the flow in normal aortic arch, the flow velocity decreases with an increase in pressure, wall shear stress, and vorticity around both anomalies. The presence of wall shear stresses in the trailing indentation region and aorta coarctation opposing the primary flow direction suggests that there exist recirculation zones in the aorta. The discrepancy in relative flowrates through the top and bottom of the aorta outlets, and the pressure drop across the coarctation, implies a high blood pressure in the upper body and a low blood pressure in the lower body. We propose using flow manipulators prior to the aortic arch and coarctation to lower the wall shear stress, while making the recirculation regions both smaller and weaker. The flow manipulators form a guide to divert and correct blood flow in critical regions of the aorta with anomalies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.202

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)0.0000.000

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 head0.026
GPT teacher head0.259
Teacher spread0.233 · 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 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

Citations8
Published2020
Admission routes1
Has abstractyes

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