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Record W4307948283 · doi:10.3390/app122110946

Whole-Heart Assessment of Turbulent Kinetic Energy in the Repaired Tetralogy of Fallot

2022· article· en· W4307948283 on OpenAlexafffund
Ashifa Hudani, James A. White, Steven C. Greenway, Julio García

Bibliographic record

VenueApplied Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsAlberta Children's HospitalLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersUniversity of Calgary
KeywordsTetralogy of FallotMedicineVentricleCardiologyInternal medicineHemodynamicsMagnetic resonance imagingTurbulence kinetic energyRadiologyHeart diseaseTurbulence

Abstract

fetched live from OpenAlex

Approximately 10% of congenital heart diseases (CHDs) include Tetralogy of Fallot (TOF). Fortunately, due to advanced surgical techniques, most patients survive until adulthood. However, these patients require frequent monitoring for postoperative complications leading to heart hemodynamic alterations. Turbulent kinetic energy (TKE), as derived from 4D-flow magnetic resonance imaging (4D-flow MRI), has been used to characterize abnormal heart hemodynamics in CHD. Hence, this study aimed to assess the difference in TKE between patients with repaired TOF (rTOF) and healthy volunteers. A total of 35 subjects, 17 rTOF patients and 18 controls, underwent standard-of-care cardiac MRI and research 4D-flow MRI using a clinical 3T scanner. Heart chambers and great vessels were segmented using 3D angiograms derived from 4D-flow MRI. The TKE was quantified within segmented volumes. TKE was compared to standard cardiac MRI metrics. Controls demonstrated higher TKE in the left atria and left ventricle. However, patients demonstrated higher TKE in the right atria, right ventricle (p < 0.05), and pulmonary artery. Lastly, no correlation was observed between TKE and standard clinical measurements. TKE can be a key indicator of the abnormal hemodynamics present in patients with rTOF and can assist future interventions and help monitor long-term outcomes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.028
GPT teacher head0.304
Teacher spread0.276 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

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