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Record W2135297623 · doi:10.1002/pd.1318

The effect of fetal gender on the false-positive rate of Down syndrome by maternal serum screening

2005· article· en· W2135297623 on OpenAlexaffabout
Valerie Martina Mueller, Tianhua Huang, Ann Summers, Stephanie Winsor

Bibliographic record

VenuePrenatal Diagnosis · 2005
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsNorth York General HospitalMcMaster University
Fundersnot available
KeywordsDown syndromeEstriolFetusMedicineObstetricsFalse positive rateGestational agePregnancyGynecologyInternal medicinePhysiologyHormoneBiology

Abstract

fetched live from OpenAlex

OBJECTIVES: (1) To further explore if there is a difference in maternal serum levels of alpha-fetoprotein (AFP), human chorionic gonadotrophin (hCG) and estriol (uE3) between fetal genders. (2) To determine if these differences influence false-positive rates of Down syndrome screening in pregnancies with male or female fetuses. METHODS: This is a descriptive study of women screened at the Ontario Maternal Serum Screening program between 1993 and 1995. The women were grouped by fetal gender and ethnicity. Serum levels of the three markers and screening false-positive rates for Down syndrome were compared between fetal genders in women of different ethnicity respectively. RESULTS: Complete data were available for 110 306 pregnancies. In all three ethnic groups, MSAFP levels were significantly decreased and MShCG levels were significantly increased in women with female fetuses. The level of MSuE3 was similar between genders. The difference in false-positive rates of Down syndrome between genders was not statistically significant. CONCLUSIONS: This is the largest study comparing false-positive rates between fetal genders. In contrast to previous studies, the differences in the serum marker levels between fetal genders do not influence the false-positive rates for Down syndrome.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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

Citations10
Published2005
Admission routes2
Has abstractyes

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