Reproductive risk factors and oestrogen/progesterone receptor-negative breast cancer in the Breast Cancer Family Registry
Bibliographic record
Abstract
BACKGROUND: Oestrogen receptor (ER)- and progesterone receptor (PR)-negative (ER-PR-) breast cancer is associated with poorer prognosis compared with other breast cancer subtypes. High parity has been associated with an increased risk of ER-PR- cancer, but emerging evidence suggests that breastfeeding may reduce this risk. Whether this potential breastfeeding benefit extends to women at high risk of breast cancer remains critical to understand for prevention. METHODS: Using population-based ascertained cases (n=4011) and controls (2997) from the Breast Cancer Family Registry, we examined reproductive risk factors in relation to ER and PR status. RESULTS: High parity (≥3 live births) without breastfeeding was positively associated only with ER-PR- tumours (odds ratio (OR)=1.57, 95% confidence interval (CI), 1.10-2.24); there was no association with parity in women who breastfed (OR=0.93, 95% CI 0.71-1.22). Across all race/ethnicities, associations for ER-PR- cancer were higher among women who did not breastfeed than among women who did. Oral contraceptive (OC) use before 1975 was associated with an increased risk of ER-PR- cancer only (OR=1.32, 95% CI 1.04-1.67). For women who began OC use in 1975 or later there was no increased risk. CONCLUSIONS: Our findings support that there are modifiable factors for ER-PR- breast cancer and that breastfeeding in particular may mitigate the increased risk of ER-PR- cancers seen from multiparity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".