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Record W1572394793 · doi:10.1002/pd.4145

Early anatomy ultrasound in women at increased risk of fetal anomalies

2013· article· en· W1572394793 on OpenAlexaff
Janice Lim, Wendy Whittle, Yee‐Man Lee, Greg Ryan, Tim Van Mieghem

Bibliographic record

VenuePrenatal Diagnosis · 2013
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineUltrasoundFetusPopulationGestationFetal echocardiographyTrisomyAnatomyPrenatal diagnosisRadiologyObstetricsPregnancyBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study was designed to assess the accuracy of ultrasound anatomy screening before 17 weeks gestation in a population at high risk of fetal anomalies. METHODS: Retrospective review of anatomy ultrasound examinations carried out between 12-17 weeks gestation in a high-risk population. Early sonographic findings were compared with the 18-22 week anatomy ultrasound, karyotype, echocardiogram and postnatal/postmortem results. RESULTS: A complete anatomical survey was achieved in 68 of 101 screened fetuses (67%), with cardiac anatomy having the lowest completion rate (78/101; 77%). Anomalies were suspected on ultrasound in 23 fetuses. Four of these did not undergo pathologic examination but had clearly abnormal findings on ultrasound. Eighteen fetuses had a confirmed abnormal outcome. Sensitivity of early anatomy ultrasound was 83.3% (n = 15/18) and specificity 94.9% (n = 75/79). There were 3 false negative ultrasounds (16.6%: trisomy 21 with short humerus, choanal atresia and ventriculomegaly, and a ventricular septal defect). False positive rate was 4.0% (4 ventricular septal defects). CONCLUSION: The high rate of visualization of anatomic structures between 12-17 weeks gestation allows for either early detection of fetal anomalies or parental reassurance in many cases. Subtle anomalies of the heart remain difficult to diagnose.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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.

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

Citations17
Published2013
Admission routes1
Has abstractyes

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