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Record W2038846445 · doi:10.1159/000165697

Increased Nuchal Translucency Thickness: A Potential Indicator for Ritscher-Schinzel Syndrome

2008· article· en· W2038846445 on OpenAlexaff
Alison Rusnak, Marie I. Hadfield, Albert E. Chudley, Sandra L. Marles, Gregory J. Reid, Bernard N. Chodirker

Bibliographic record

VenueFetal Diagnosis and Therapy · 2008
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineUltrasoundFetusNuchal Translucency MeasurementFirst trimesterPregnancyObstetricsRadiologyGenetics

Abstract

fetched live from OpenAlex

OBJECTIVE: The Ritscher-Schinzel syndrome (RSS), also known as the 3C syndrome, is an autosomal recessive disorder classically comprising craniofacial, cerebellar and cardiac defects. The underlying molecular etiology remains unknown; therefore, prenatal diagnosis of recurrences depends on identification of the associated structural anomalies on second trimester ultrasound examination. Identification of recurrences using first-trimester ultrasound has not been reported previously. METHODS: Two women who presented at our center with fetal nuchal abnormalities on first trimester ultrasound went on to have children with RSS. One of the women had also undergone a previous pregnancy termination for fetal anomalies consistent with RSS. The ultrasound findings and details of these 3 cases were reviewed. RESULTS: Both cases of RSS and the third suspected case were found to have nuchal abnormalities on first-trimester scan. All went on to develop malformations consistent with RSS detectable on second-trimester ultrasound. The later 2 cases continued to term and the children had facial characteristics consistent with RSS. CONCLUSION: First-trimester ultrasound assessment of nuchal translucency could be considered as a method for identifying sib recurrences of RSS. In addition, RSS should be on the differential diagnosis when increased nuchal translucency is seen on first-trimester scan.

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)0.000
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 score0.251
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.027
GPT teacher head0.253
Teacher spread0.226 · 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

Citations9
Published2008
Admission routes1
Has abstractyes

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