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Record W2053948052 · doi:10.1088/1748-6041/2/1/s05

Time-dependent 3D simulations of the hemodynamics in a stented coronary artery

2007· article· en· W2053948052 on OpenAlexafffund
Isam Faik, Rosaire Mongrain, Richard L. Leask, Josep Rodés‐Cabau, Éric Larose, Olivier F. Bertrand

Bibliographic record

VenueBiomedical Materials · 2007
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversité LavalMontreal Heart InstituteMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaInstitut de Cardiologie de Montréal
KeywordsRestenosisHemodynamicsStentShear stressCardiologyBlood flowInternal medicineMedicineArteryCoronary arteriesMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Stenting is becoming the major interventional cardiology procedure worldwide. However restenosis remains a major limitation to the effectiveness of stents. Alterations to the local hemodynamics in the stented segment of the artery could be a potential factor in the development of in-stent restenosis. The characterization of wall shear stress and of blood flow patterns in a stented artery is therefore necessary for a good understanding of the role of hemodynamics in the development of in-stent restenosis. We have used a time-dependent 3D numerical model of a stented coronary artery to study the characteristics of the blood flow and the shear stress distribution. Our results show that the presence of the stent produces significant secondary flow that is limited to an annulus in the near wall region. Low shear stress zones were localized in the vicinity of the struts while the tips of the struts exhibited high values of shear stress. These results support the hypothesis that local hemodynamics may affect the development of in-stent restenosis and could influence the choice of stent geometries for future stent designs.

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)0.001
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.281
Teacher spread0.268 · 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

Citations28
Published2007
Admission routes2
Has abstractyes

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