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Record W2332989260 · doi:10.1115/sbc2008-192962

Stereoscopic PIV of Steady Flow Through a Bileaflet Mechanical Heart Valve

2008· article· en· W2332989260 on OpenAlexafffund
Chris Hutchison, Pierre E. Sullivan, C. Ross Ethier

Bibliographic record

VenueASME 2008 Summer Bioengineering Conference, Parts A and B · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsParticle image velocimetryTurbulenceFlow (mathematics)Heart valveMechanicsReynolds numberMechanical heartMaterials sciencePhysicsCardiologyMedicine

Abstract

fetched live from OpenAlex

Each year over 180,000 mechanical heart valves are implanted worldwide, with the bileaflet mechanical heart valve (BiMHV) accounting for approximately 85% of all valve replacements [1,2]. Although much improved from previous valve designs, aortic BiMHV design is far from ideal, and serious complications such as thromboembolism and hemolysis often result. Hemolysis and platelet activation are thought to be caused by turbulent Reynolds shear stresses in the flow [1]. Numerous previous studies have examined aortic BiMHV flow using LDA and two component Particle Image Velocimetry (PIV), and have shown the flow to be complex and three-dimensional [3,4]. Stereoscopic PIV (SPIV) can obtain all three velocity components on a flow plane, and hence has the potential to provide better understanding of three dimensional flow characteristics. The objective of the current study was to use SPIV to measure steady flow, including turbulence properties, downstream of a BiMHV in a modeled aorta. The resulting dataset will be useful for CFD model validation, and the intent is to make it publicly available.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.043
GPT teacher head0.311
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueASME 2008 Summer Bioengineering Conference, Parts A and BSame topicCardiac Valve Diseases and TreatmentsFrench-language works237,207