Human Chorionic Gonadotropin and Alpha-Fetoprotein Concentrations in Pregnancy and Maternal Risk of Breast Cancer: A Nested Case-Control Study
Bibliographic record
Abstract
Pregnancy hormones are believed to be involved in the protection against breast cancer conferred by pregnancy. The authors explored the association of maternal breast cancer with human chorionic gonadotropin (hCG) and alpha-fetoprotein (AFP). In 2001, a case-control study was nested within the Northern Sweden Maternity Cohort, an ongoing study in which blood samples have been collected from first-trimester pregnant women since 1975. Cases (n = 210) and controls (n = 357) were matched for age, parity, and date of blood donation. Concentrations of hCG and AFP were measured by immunoassay. No overall significant association of breast cancer with either hCG or AFP was observed. However, women with hCG levels in the top tertile tended to be at lower risk of breast cancer than women with hCG levels in the lowest tertile in the whole study population and in subgroups of age at sampling, parity, and age at cancer diagnosis. A borderline-significant decrease in risk with high hCG levels was observed in women who developed breast cancer after the median lag time to cancer diagnosis (> or =14 years; odds ratio = 0.53, 95% confidence interval: 0.27, 1.03; P = 0.06). These findings, though very preliminary, are consistent with a possible long-term protective association of breast cancer risk with elevated levels of circulating hCG in the early stages of pregnancy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".