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How pulmonary valve regurgitation after tetralogy of fallot repair changes the flow dynamics in the right ventricle: An <i>in vitro</i> study

2020· article· en· W3044332186 on OpenAlexafffund
Amanda Mikhail, Giuseppe Di Labbio, Ahmed Darwish, Lyes Kadem

Bibliographic record

VenueMedical Engineering & Physics · 2020
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTetralogy of FallotVentricleCardiologyInternal medicineRegurgitation (circulation)MedicinePulmonary valveTricuspid valveHeart disease

Abstract

fetched live from OpenAlex

Tetralogy of Fallot is the most common cyanotic congenital disease, affecting 10% of children with congenital heart disease. The surgical management of patients with Tetralogy of Fallot leads, however, to significant detrimental effects on the right ventricle including pulmonary valve regurgitation. This experiment aimed to simulate different cases of pulmonary valve regurgitation with varying degrees of severity in order to observe the changes in flow structures present in the right ventricle. Planar time-resolved particle image velocimetry measurements have been performed on a custom-made double activation simulator reproducing flow conditions in a model of a right ventricle. Changes in flow characteristics in the right ventricle have been evaluated in terms of velocity fields and profiles, tricuspid inflow jet orientation and viscous energy dissipation. Our results show that pulmonary valve regurgitation significantly alters the flow in the right ventricle mostly by impairing the diastolic inflow through the tricuspid valve and by increasing viscous energy loss. This fundamental work should allow for a better understanding of such changes in the RV flow dynamics. It may also help in developing new strategies allowing for a better follow-up of patients with repaired TOF and for decision-making in terms of pulmonary valve replacement.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

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