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Record W4396761452 · doi:10.3390/jcdd11050147

Management of Ebstein Anomaly in the Current Era: The Story of One Fetus and the Collaboration of Many—A Case Report

2024· article· en· W4396761452 on OpenAlexaff
Ann Kavanaugh‐McHugh, Lisa C. Zuckerwise, Stacy A. S. Killen, Emily A. Morris, Rachel T. Sullivan, Mhd Wael Alrifai, David P. Bichell, Melissa Smith-Parrish, Lindsay R. Freud

Bibliographic record

VenueJournal of Cardiovascular Development and Disease · 2024
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineEbstein's anomalyFetusIntensive care medicineTransplacentalPregnancySurgeryTricuspid valve

Abstract

fetched live from OpenAlex

Collaborative multicenter research has significantly increased our understanding of fetal Ebstein anomaly, delineating risk factors for adverse outcomes as well as predictors of postnatal management. These data are incorporated into prenatal care and therapeutic strategies and inform family counseling and delivery planning to optimize care. This report details the translation of findings from multicenter studies into multidisciplinary prenatal care for a fetus with Ebstein anomaly, supraventricular tachycardia, and a circular shunt, including transplacental therapy to control arrhythmias and achieve ductal constriction, informed and coordinated delivery room management, and planned univentricular surgical palliation.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0030.004
Open science0.0020.003
Research integrity0.0080.010
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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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 designCase report
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

Citations2
Published2024
Admission routes1
Has abstractyes

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