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Assessment of structural and hemodynamic performance of vascular stents modelled as periodic lattices

2018· article· en· W2804050024 on OpenAlexaff
Purnendu K.M. Prithipaul, Michael Kokkolaras, Damiano Pasini

Bibliographic record

VenueMedical Engineering & Physics · 2018
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsMcGill University
Fundersnot available
KeywordsStentHemodynamicsRestenosisMaterials scienceStiffnessShear stressRepresentative elementary volumeStructural engineeringTopology (electrical circuits)Computer scienceFinite element methodMathematicsEngineeringComposite materialMedicineCardiologyRadiology

Abstract

fetched live from OpenAlex

This work considers vascular stents with tubular geometry assumed to follow a periodic arrangement of repeating unit cells. Structural and hemodynamic metrics are presented to assess alternative stent geometries, each defined by the topology of the unit cell. Structural metrics include foreshortening, elastic recoil and radial stiffness, whereas hemodynamic performance is described by a wall shear stress index quantifying the impact of in-stent restenosis. A representative volume element (RVE) modelling approach is used, and results are compared to those obtained from full simulations of entire stents. We demonstrate that the RVE approach can be used to quantify the impact of the topology of the repeating unit on the structural and hemodynamic properties of a stent, and thus support clinicians in making proper choices among alternative stent geometries.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.271
Teacher spread0.264 · 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

Citations43
Published2018
Admission routes1
Has abstractyes

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