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Record W2973192998 · doi:10.1002/jum.15124

Natural History of Ventriculomegaly in Fetal Agenesis of the Corpus Callosum

2019· article· en· W2973192998 on OpenAlexaff
Sophie Masmejan, Susan Blasér, Johannes Keunen, Gareth Seaward, Rory Windrim, Edmond Kelly, Greg Ryan, David Baud, Tim Van Mieghem

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids FoundationMount Sinai Hospital
Fundersnot available
KeywordsVentriculomegalyMedicineCorpus callosumFetusCorpus Callosum AgenesisLateral ventriclesUltrasoundPregnancyAgenesisGestational ageAgenesis of the corpus callosumNatural historyGestationObstetricsAnatomyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the natural evolution of the size of the fetal lateral ventricles throughout pregnancy in fetuses with callosal anomalies. METHODS: Cases of fetal callosal anomalies were retrospectively classified as isolated or complex based on the presence of other structural or genetic anomalies. Longitudinal ultrasound studies were reviewed, and postnatal outcomes were retrieved for isolated cases. RESULTS: In 135 fetuses, those who first presented after 24 weeks' gestation were more likely to have ventriculomegaly (n = 58 of 68 [85%]) than those who presented before 24 weeks (n = 39 of 67 [58%]; P < .001). In 79 cases that had longitudinal follow-up, the mean increase in ventricular width was 0.6 mm/wk, without a significant difference between isolated and complex cases (mean ± SD, 0.6 ± 1.5 versus 0.6 ± 1.1 mm; P = .45). CONCLUSIONS: Callosal anomalies are associated with progressive ventriculomegaly on prenatal ultrasound imaging, without a difference between isolated and complex anomalies. This feature should be considered part of the disease spectrum. The consequence of progressive ventriculomegaly on the long-term neurodevelopmental outcome is still unknown, and further studies should be aimed at obtaining long-term follow-up of these cases.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.140
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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 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

Citations11
Published2019
Admission routes1
Has abstractyes

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