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Abstract 15868: Title: Accuracy of Fetal Echocardiography in the Antenatal Diagnosis of Three Common Conotruncal Defects

2020· article· en· W3101899878 on OpenAlexaff
Rose He, Jayani Abeysekera, Kim Haberer, Aisling Young, Luke Eckersley, Angela McBrien, Isabella Adatia, Rishav Sharma, Michelle Rushfeldt, Lisa K. Hornberger

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsStollery Children's HospitalRoyal Alexandra HospitalUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsMedicineTetralogy of FallotTruncus arteriosusFetal echocardiographyPrenatal diagnosisVentricular outflow tractDouble outlet right ventriclePolyhydramniosRadiologyAutopsyHeart diseaseFetusCardiologyStenosisPregnancyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: The prenatal diagnosis of conotruncal defects (CTDs) has improved over the past decade, particularly with inclusion of outflow tract imaging at routine ultrasound. Appropriate prenatal counseling for CTDs demands an accurate diagnosis, as even more subtle cardiac pathology could complicate clinical outcomes. We sought to determine the anatomical accuracy of fetal echocardiography in evaluating common CTDs and factors that contribute to accuracy. Methods: All cases of tetralogy of Fallot(TOF), double outlet right ventricle(DORV) and truncus arteriosus(TA) encountered in our institution from 2007-2018 were reviewed. Discrepancies in anatomical findings between prenatal (most accurate exam) and postnatal (echo/surgery) or autopsy exams were categorized as: C1) no difference C2) minor difference with no impact on outcome (e.g.aberrant right subclavian artery), C3) minor difference that could make a minor difference to the delivery plan or surgery (e.g.branch pulmonary artery stenosis), C4) major difference that changes the course of the pregnancy, delivery or surgical planning (e.g.ductal dependency). Results: Of the 255 CTD cases, 162 had prenatal and postnatal and/or autopsy data available. Of the 162, 107(65.6%) fit C1, 35(21.5%) C2, 12(7.4%) C3, and 8(5.5%) C4. The greatest accuracy was observed in TOF, with 69/71(97.2%) in C1 and C2 versus 56/69(81.2%) in DORV and 16/22(72.7%) in TA(p=0.003). Excluding 5 cases at 10-16weeks, there was a tendency towards a greater proportion in C1 and C2 when examined at 17-23 weeks (60/64, 93.4%) versus 24-32 weeks (50/57, 87.7%) and >32 weeks (28/36, 77.8%) (p=0.06). Era of assessment also revealed a difference with 43/55(78.2%) of studies performed from 2007-2011 in C1 and C2 versus 99/107(92.5%) from 2012-2018 (p=0.01).When we compared pregnancies with one versus serial exams, we observed a lower proportion of C4 cases from 9.6% to 2.7%, respectively. In those with serial exams, 20% had achieved C1 or C2 only at serial exam. Conclusions: The diagnostic accuracy of fetal echocardiography in CTDs is generally high, especially for TOF and when performed at 17-24weeks. There has been significant improvement in accuracy since 2011. Serial exams potentially improve diagnostic accuracy.

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)0.014
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.038
GPT teacher head0.289
Teacher spread0.250 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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