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Record W1991408158 · doi:10.1155/2012/850313

Phytoestrogens in Human Pregnancy

2012· article· en· W1991408158 on OpenAlexaff
John Jarrell, Warren G. Foster, David W. Kinniburgh

Bibliographic record

VenueObstetrics and Gynecology International · 2012
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsMcMaster UniversityUniversity of Calgary
Fundersnot available
KeywordsPhytoestrogensPregnancyMedicineAmniotic fluidObstetricsPhysiologyAmniocentesisInfertilityOffspringFetusLactationGynecologyEndocrinologyEstrogenBiologyPrenatal diagnosis

Abstract

fetched live from OpenAlex

Background. The hormonal milieu associated with pregnancy has become a focus of interest owing to potential links with the developmental origins of health and disease. Phytoestrogens are hormonally active plant-derived chemicals that may have an impact on human reproductive processes. However, developmental exposure to phytoestrogens has not been well characterized and thus our objective was to quantify phytoestrogen exposure during pregnancy and lactation. Methods. Women in the second trimester of pregnancy entered the study during counseling for prenatal genetic information. Women who had an indication for a genetic amniocentesis on the basis of late maternal age were approached for inclusion. They completed an environmental questionnaire; a sample of amniotic fluid was collected for karyotype, blood was collected from women during pregnancy and at birth, from the umbilical cord and breast milk. Samples were tested for the presence of daidzein and genistein by GC Mass Spectroscopy. Findings. Phytoestrogens are commonly found in pregnant women's serum and amniotic fluid during pregnancy. There is a sex difference in the concentrations with higher levels in amniotic fluid containing female fetuses. This difference was not present in maternal serum. Soy ingestion increases amniotic fluid phytoestrogen concentrations in female and male fetuses. The presence and concentrations of phytoestrogens did not differ in relation to common pregnancy complications or preexisting infertility.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.027
GPT teacher head0.336
Teacher spread0.309 · 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

Citations35
Published2012
Admission routes1
Has abstractyes

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