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Record W2122752125 · doi:10.1109/ultsym.2007.257

11C-5 Characterization of Time-Varying Mechanical Viscoelastic Parameters of Mimicking Deep Vein Thrombi with 2D Dynamic Elastography

2007· article· en· W2122752125 on OpenAlexaff
Cédric Schmitt, Anis Hadj Henni, Guy Cloutier

Bibliographic record

VenueProceedings/Proceedings - IEEE Ultrasonics Symposium · 2007
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsViscoelasticityElastographyMaterials scienceElasticity (physics)Biomedical engineeringAcousticsShear wavesUltrasoundShear (geology)Composite materialPhysicsMedicine

Abstract

fetched live from OpenAlex

Staging mechanical properties of deep vein thrombi (elasticity and viscosity) can be of importance for therapy planning because the compactness of a blood clot impacts the efficiency of thrombolysis drugs. This article proposes the dynamic vascular elastography (DVE) method to solve this problem. It consists to retrieve viscoelastic parameters of 8-mm diameter blood clot cylindrical inclusions from shear wave propagation characteristics. The technique firstly implies the generation of a low frequency (50-190 Hz) harmonic plane shear wave in the medium and the tracking of this wave with an ultra- fast ultrasound scanner (frame rate > 3000 Hz). An inverse problem was formulated as a least-square minimization between simulations and experimental results of viscoelasticity. The wave excitation technique also permitted to do a multi-frequency analysis to validate the Voigt's model as a valid approach to represent the viscoelasticity of blood clots. DVE proved to have sufficient sensitivity to follow the time-varying blood coagulation process and to differentiate mechanical properties of blood samples with different hematocrits.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.237
Teacher spread0.230 · 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.

Study designBench or experimental
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

Citations12
Published2007
Admission routes1
Has abstractyes

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