Phytoestrogens in Human Pregnancy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".