Hormonal factors and the risk of breast cancer according to estrogen- and progesterone-receptor subgroup.
Bibliographic record
Abstract
Evidence suggests hormonal factors may be more strongly associated with estrogen receptor+progesterone receptor+ (ER+PR+) than ER-PR- breast cancer risk. This study evaluated risk factors according to ERPR tumor status among pre- and postmenopausal women participating in two recent population-based case-control studies. Breast cancer cases, ages 25-74 years, and diagnosed 1995-1998 were sampled from the Ontario Cancer Registry. Controls were a random sample of women identified using the Ontario Ministry of Finance rolls and were frequency-matched to cases within 5-year age groups. Epidemiological data were collected from breast cancer cases and controls using two self-administered questionnaires. ERPR data were obtained for 87% of the breast cancer cases (3,276 of 3,748). Multivariate polytomous logistic regression was used to obtain odds ratios estimates and 95% confidence intervals. The following significant differences were observed in the risk factor profiles for ER+PR+ and ER-PR- breast cancer: among premenopausal women, late age at menarche was only associated with a reduction in ER+PR+ breast cancer risk; obesity was associated with an increased ER-PR- and decreased ER+PR+ cancer risk; and the association between alcohol intake and breast cancer risk was heterogeneous across ERPR subgroups, although the direction varied across the levels of alcohol intake. Among postmenopausal women, there were no statistically significant differences observed in the risk factor profiles for ER+PR+ and ER-PR- breast cancer. Some heterogeneity exists in the risk factor profiles of ER+PR+ and ER-PR- premenopausal breast cancer; however, risk factor profiles did not differ markedly for postmenopausal breast cancer.
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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 | 0.002 |
| 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) | 0.001 | 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".