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Record W1993181559 · doi:10.1364/boe.5.004405

Evaluation of flow velocities after carotid artery stenting through split spectrum Doppler optical coherence tomography and computational fluid dynamics modeling

2014· article· en· W1993181559 on OpenAlexafffund
Barry Vuong, Helen Genis, Ronnie Wong, Joel Ramjist, Jamil Jivraj, Hamza Farooq, Cuiru Sun, Victor X. D. Yang

Bibliographic record

VenueBiomedical Optics Express · 2014
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsSunnybrook Health Science CentreToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOptical coherence tomographyHemodynamicsBlood flowDoppler effectStentFlow velocityComputational fluid dynamicsBiomedical engineeringInternal carotid arteryBifurcationCommon carotid arteryMedicineCoronary artery diseaseRadiologyFlow (mathematics)CardiologyCarotid arteriesPhysicsMechanics

Abstract

fetched live from OpenAlex

Hemodynamics plays a critical role in the development of atherosclerosis, specifically in regions of curved vasculature such as bifurcations exhibiting irregular blood flow profiles. Carotid atherosclerotic disease can be intervened by stent implantation, but this may result in greater alterations to local blood flow and consequently further complications. This study demonstrates the use of a variant of Doppler optical coherence tomography (DOCT) known as split spectrum DOCT (ssDOCT) to evaluate hemodynamic patterns both before and after stent implantation in the bifurcation junction in the internal carotid artery (ICA). Computational fluid dynamics (CFD) models were constructed to simulate blood velocity profiles and compared to the findings achieved through ssDOCT images. Both methods demonstrated noticeable alterations in hemodynamic patterns following stent implantation, with features such as slow velocity regions at the neck of the bifurcation and recirculation zones at the stent struts. Strong correlation between CFD models and ssDOCT images demonstrate the potential of ssDOCT imaging in the optimization of stent implantation in the clinical setting.

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)0.000
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 score0.697
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.256
Teacher spread0.238 · 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

Citations14
Published2014
Admission routes2
Has abstractyes

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