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Record W2996184704 · doi:10.1002/pd.5626

Current and future role of fetal cardiovascular MRI in the setting of fetal cardiac interventions

2019· review· en· W2996184704 on OpenAlexaff
Davide Marini, Jiawei Xu, Liqun Sun, Edgar Jaeggi, Mike Seed

Bibliographic record

VenuePrenatal Diagnosis · 2019
Typereview
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineFetusOligohydramniosFetal circulationPsychological interventionIn uteroIntensive care medicineFetal echocardiographyMagnetic resonance imagingIntervention (counseling)PregnancyPrenatal diagnosisRadiologyPlacenta

Abstract

fetched live from OpenAlex

Over recent years, technical developments resulting in the feasibility of fetal cardiovascular magnetic resonance (CMR) have provided a new diagnostic tool for studying the human fetal heart and circulation. During the same period, we have witnessed the arrival of several minimally invasive fetal cardiac interventions (FCI) as a possible form of treatment in selected congenital heart diseases (CHDs). The role of fetal CMR in the planning and monitoring of FCI is not yet clear. Indeed, high-quality fetal CMR is not available or routinely offered at most centers caring for patients with prenatally detected CHD. However, in theory, fetal CMR could have much to offer in the setting of FCI by providing complementary anatomic and physiologic information relating to the specific intervention under consideration. Similarly, fetal CMR may be useful as an alternative imaging modality when ultrasound is hampered by technical limitations, for example, in the setting of oligohydramnios and in late gestation. In this review, we summarize current experience of the use of fetal CMR in the diagnosis and monitoring of fetuses with cardiopathies in the setting of a range of invasive in utero cardiac and vascular interventions and medical treatments and speculate about future directions for this versatile imaging medium.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.313
Teacher spread0.279 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2019
Admission routes1
Has abstractyes

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