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Record W2981244647 · doi:10.1002/ccd.28507

Use of 3D rotational angiography to perform computational fluid dynamics and virtual interventions in aortic coarctation

2019· article· en· W2981244647 on OpenAlexaff
Aimee K. Armstrong, Jeffrey D. Zampi, Lucian Itu, Lee Benson

Bibliographic record

VenueCatheterization and Cardiovascular Interventions · 2019
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsHospital for Sick Children
FundersSiemens Medical Solutions USA
KeywordsMedicineCoarctation of the aortaHemodynamicsComputational fluid dynamicsRotational angiographyAngiographyCardiologyPsychological interventionAortaInternal medicineBlood flowRadiologyMechanics

Abstract

fetched live from OpenAlex

Computational fluid dynamics (CFD) can be used to analyze blood flow and to predict hemodynamic outcomes after interventions for coarctation of the aorta and other cardiovascular diseases. We report the first use of cardiac 3-dimensional rotational angiography for CFD and show not only feasibility but also validation of its hemodynamic computations with catheter-based measurements in three patients.

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 score0.339
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.027
GPT teacher head0.281
Teacher spread0.254 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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