Reproductive factors and risk of hormone receptor positive and negative breast cancer: a cohort study
Bibliographic record
Abstract
BACKGROUND: The association of reproductive factors with hormone receptor (HR)-negative breast tumors remains uncertain. METHODS: Within the EPIC cohort, Cox proportional hazards models were used to describe the relationships of reproductive factors (menarcheal age, time between menarche and first pregnancy, parity, number of children, age at first and last pregnancies, time since last full-term childbirth, breastfeeding, age at menopause, ever having an abortion and use of oral contraceptives [OC]) with risk of ER-PR- (n = 998) and ER+PR+ (n = 3,567) breast tumors. RESULTS: A later first full-term childbirth was associated with increased risk of ER+PR+ tumors but not with risk of ER-PR- tumors (≥35 vs. ≤19 years HR: 1.47 [95% CI 1.15-1.88] p(trend) < 0.001 for ER+PR+ tumors; ≥35 vs. ≤19 years HR: 0.93 [95% CI 0.53-1.65] p(trend) = 0.96 for ER-PR- tumors; P(het) = 0.03). The risk associations of menarcheal age, and time period between menarche and first full-term childbirth with ER-PR-tumors were in the similar direction with risk of ER+PR+ tumors (p(het) = 0.50), although weaker in magnitude and statistically only borderline significant. Other parity related factors such as ever a full-term birth, number of births, age- and time since last birth were associated only with ER+PR+ malignancies, however no statistical heterogeneity between breast cancer subtypes was observed. Breastfeeding and OC use were generally not associated with breast cancer subtype risk. CONCLUSION: Our study provides possible evidence that age at menarche, and time between menarche and first full-term childbirth may be associated with the etiology of both HR-negative and HR-positive malignancies, although the associations with HR-negative breast cancer were only borderline significant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".