Effects of Parity on Pregnancy Hormonal Profiles Across Ethnic Groups with a Diverse Incidence of Breast Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Epidemiologic evidence suggests that a full-term pregnancy may affect maternal risk of breast cancer later in life. The objective of this cross-sectional study was to compare circulating levels of maternal hormones affecting breast differentiation (human chorionic gonadotropin and prolactin) and proliferation [alpha-fetoprotein, insulin-like growth factor I (IGF-I), and estradiol] between women at a low to moderate risk (Asians and Hispanics), as compared with women at a high risk for breast cancer (Caucasians and African-Americans). Between May 2002 and December 2004, a total of 586 pregnant women were approached during a routine prenatal visit. Among them, 450 women (206 Caucasian, 126 Asian, 88 Hispanic, and 30 African-American) met the inclusion criteria and signed the informed consent. Only singleton pregnancies were considered. Blood samples were drawn during the second trimester of pregnancy. Laboratory analyses were done using the IMMULITE 2000 immunoassay system. Gestational age standardized mean levels of estradiol, IGF-I, and prolactin were significantly higher in Hispanic women compared with Caucasian women. Mean concentration of IGF-I was significantly higher in African-American women compared with Caucasian and Asian women. No significant differences in pregnancy hormone levels were observed between Caucasian and Asian (predominantly second-generation Chinese) women in this study. Irrespective of ethnicity, women who had their first pregnancy had substantially higher mean levels of alpha-fetoprotein, human chorionic gonadotropin, estradiol, and prolactin compared with women who previously had at least one full-term pregnancy. These data suggest that circulating pregnancy hormone levels may explain some of the ethnic differences in breast cancer risk.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it