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Record W2082379038 · doi:10.1115/sbc2013-14749

Development of a 1D Model for Assessing the Aortic Root Pressure Drop With Viscosity and Compliance

2013· article· en· W2082379038 on OpenAlexaff
Hossein Mohammadi, Raymond Cartier, Rosaire Mongrain

Bibliographic record

VenueVolume 1A: Abdominal Aortic Aneurysms; Active and Reactive Soft Matter; Atherosclerosis; BioFluid Mechanics; Education; Biotransport Phenomena; Bone, Joint and Spine Mechanics; Brain Injury; Cardiac Mechanics; Cardiovascular Devices, Fluids and Imaging; Cartilage and Disc Mechanics; Cell and Tissue Engineering; Cerebral Aneurysms; Computational Biofluid Dynamics; Device Design, Human Dynamics, and Rehabilitation; Drug Delivery and Disease Treatment; Engineered Cellular Environments · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsMontreal Heart InstituteMcGill University
Fundersnot available
KeywordsStenosisCardiologyInternal medicineMedicineAortic valveAortic valve stenosisBody orificeRegurgitation (circulation)Blood flowPressure dropAnatomyMechanics

Abstract

fetched live from OpenAlex

Aging and some pathologies such as arterial hypertension, diabetes, hyperglycemia, and hyperinsulimenia cause some geometrical and mechanical changes in the aortic valve microstructure. Cupsal thickening and lost of extensibility (increasing stiffness) are the consequences of these changes in the aortic valve which have a negative impact on the function of the valve [1]. The most frequent form of diseases of the aortic valve is the calcific aortic stenosis which is responsible for 80% of the North American deaths due to valvular heart diseases [2]. In this pathology, calcified nodules on the valve leaflets occur which lead to the thickening and stiffening of the leaflets and restricting the natural motion of the valve which presents an increased resistance to forward blood flow during the ejection phase of the cardiac cycle. To reduce the mortality and morbidity from the aortic stenosis, clinical management and proper diagnosis are essential [3]. Tranvalvular pressure gradient (TPG) and the effective orifice area (EOA), the minimum cross sectional area of the blood flow across the stenosis, are the most commonly used indices for assessing the aortic stenosis [4]. Numerous studies have been done to relate the TPG across the stenosis to the blood flow rate and EOA. Gorlin (1951) was the first to establish a relationship between TPG and EOA [5]. Several studies have reported deviations in valve area calculation by using Gorlin formula. This formula was derived based on some assumptions such as rigid circular orifice, non viscous and steady flow, while valvular orifices are compliant and the flow through them is viscous and pulsatile [6]. Several corrections have been proposed. However, even with these improved formula, significant deviations are still reported [7]. Calark (1978), Bermejo et al (2002) and Garcia et al (2006), by presenting a theoretical model, tried to express TPG in terms of the blood flow rate and EOA [8–10]. None of these studies considered the effect of the aortic root compliance on TPG. Nobari et al reported that the stiffening of the aorta changes the pressure drop and affects the leaflets motion [11]. Therefore, the objective of this study is to develop a 1D model for assessing the aortic pressure drop for the transient viscous blood flow across the aortic stenosis, by taking into account the vessel wall compliance. The derived TPG will be expressed in terms of the surrogate variables which are anatomical and hemodynamic data meaningful and accessible for physicians.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.217
Teacher spread0.209 · 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
GenreMethods

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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueVolume 1A: Abdominal Aortic Aneurysms; Active and Reactive Soft Matter; Atherosclerosis; BioFluid Mechanics; Education; Biotransport Phenomena; Bone, Joint and Spine Mechanics; Brain Injury; Cardiac Mechanics; Cardiovascular Devices, Fluids and Imaging; Cartilage and Disc Mechanics; Cell and Tissue Engineering; Cerebral Aneurysms; Computational Biofluid Dynamics; Device Design, Human Dynamics, and Rehabilitation; Drug Delivery and Disease Treatment; Engineered Cellular Environments→Same topicCardiac Valve Diseases and Treatments→French-language works237,207→