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Record W2950162650 · doi:10.1002/jmri.26815

MR imaging of the fetal heart

2019· review· en· W2950162650 on OpenAlexaff
Davide Marini, Joshua van Amerom, Brahmdeep S. Saini, Liqun Sun, Mike Seed

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2019
Typereview
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineOligohydramniosMagnetic resonance imagingUltrasoundRadiologyFetal echocardiographyCardiac imagingFetusCardiologyPregnancyPrenatal diagnosis

Abstract

fetched live from OpenAlex

In the last decade, technological advances have enabled the acquisition of high spatial and temporal resolution cardiac magnetic resonance imaging (MRI) in the fetus. Fetal cardiac MRI has emerged as an alternative to ultrasound, which may be helpful to confirm a diagnosis of congenital heart disease when ultrasound assessment is hampered, for example in late gestation or in the setting of oligohydramnios. MRI also provides unique physiologic information, including vessel blood flow, oxygen saturation and hematocrit, which may be helpful to investigate cardiac and placental diseases. In this review, we summarize some of the main techniques and significant advances in the field to date. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2020;51:1030-1044.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.305
Teacher spread0.277 · 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 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

Citations21
Published2019
Admission routes1
Has abstractyes

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