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Record W3021115904 · doi:10.1113/jp279054

Feasibility of ventricular volumetry by cardiovascular MRI to assess cardiac function in the fetal sheep

2020· article· en· W3021115904 on OpenAlexaff
Steven K. S. Cho, Jack R. T. Darby, Brahmdeep S. Saini, Mitchell C. Lock, Stacey L. Holman, Jessie Mei Lim, Sunthara Rajan Perumal, Christopher K. Macgowan, Janna L. Morrison, Mike Seed

Bibliographic record

VenueThe Journal of Physiology · 2020
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersAustralian Research Council
KeywordsAscending aortaCardiologyVentricleMedicineStroke volumeEjection fractionInternal medicineFetusCardiac VentricleBlood flowFetal circulationMagnetic resonance imagingPulmonary arteryCardiac outputAortaHemodynamicsHeart failureRadiologyPregnancyBiology

Abstract

fetched live from OpenAlex

Key points The application of fetal cardiovascular magnetic resonance imaging (CMR) to assess fetal cardiovascular physiology and cardiac function through the quantification of ventricular volumes has previously been investigated, but the approach has not yet been fully validated. Ventricular output measurements calculated from heart rate and stroke volumes (SV) of the right and left ventricles measured by ventricular volumetry (VV) exhibited a high level of agreement with phase‐contrast (PC) blood flow measurements in the main pulmonary artery and ascending aorta, respectively. Ejection fraction of the right ventricle, which is lower than that of the left ventricle in postnatal subjects, was similar to the left ventricular ejection fraction in the fetus; probably due to the different loading conditions present in the fetal circulation. This study provides evidence to support the reliability of VV in the sheep fetus, providing evidence for its use in animal models of human diseases affecting the fetal circulation. Abstract The application of ventricular volumetry (VV) by cardiovascular magnetic resonance imaging (CMR) in the fetus remains challenging due to the small size of the fetal heart and high heart rate. The reliability of this technique in utero has not yet been established. The aim of this study was to assess the feasibility and reliability of VV in a fetal sheep model of human pregnancy. Right and left ventricular outputs by stroke volume (SV) measured using VV were compared with 2D phase‐contrast (PC) CMR measurements of blood flow in the main pulmonary artery (MPA) and ascending aorta (AAo). At 124–140 days (d) gestation, singleton bearing Merino ewes underwent CMR under general anaesthesia using fetal femoral artery catheters, implanted at 109–117d, to trigger cine steady state free precession acquisitions of ventricular short‐axis stacks. The short‐axis cine stacks were segmented at end‐systole and end‐diastole, yielding right and left ventricular SV, ejection fraction, and cardiac outputs (SV × heart rate). PC cine acquisitions of MPA and AAo were analysed to measure blood flow, which served as comparators for the right and left cardiac outputs by VV. There was good correlation and agreement between VV and PC measures of ventricular outputs with no significant bias (r2 = 0.926; P < 0.0001; Bias = −4.7 ± 10.5 ml min−1 kg−1; 95% limits of agreement: −15.9 to 25.2 ml min−1 kg−1). This study validates fetal VV by CMR in a large animal model of human pregnancy and provides preliminary reference values of fetal sheep right and left ventricles in late gestation.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.288
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 source (direct Gemma or distilled Codex), 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

Citations27
Published2020
Admission routes1
Has abstractyes

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