Prediagnosis Reproductive Factors and All-Cause Mortality for Women with Breast Cancer in the Breast Cancer Family Registry
Bibliographic record
Abstract
Studies have examined the prognostic relevance of reproductive factors before breast cancer diagnosis, but most have been small and their overall findings inconclusive. Associations between reproductive risk factors and all-cause mortality after breast cancer diagnosis were assessed with the use of a population-based cohort of 3,107 women of White European ancestry with invasive breast cancer (1,130 from Melbourne and Sydney, Australia; 1,441 from Ontario, Canada; and 536 from Northern California, United States). During follow-up with a median of 8.5 years, 567 deaths occurred. At recruitment, questionnaire data were collected on oral contraceptive use, number of full-term pregnancies, age at first full-term pregnancy, time from last full-term pregnancy to breast cancer diagnosis, breastfeeding, age at menarche, and menopause and menopausal status at breast cancer diagnosis. Hazard ratios for all-cause mortality were estimated with the use of Cox proportional hazards models with and without adjustment for age at diagnosis, study center, education, and body mass index. Compared with nulliparous women, those who had a child up to 2 years, or between 2 and 5 years, before their breast cancer diagnosis were more likely to die. The unadjusted hazard ratio estimates were 2.75 [95% confidence interval (95% CI), 1.98-3.83; P < 0.001] and 2.20 (95% CI, 1.65-2.94; P < 0.001), respectively, and the adjusted estimates were 2.25 (95% CI, 1.59-3.18; P < 0.001) and 1.82 (95% CI, 1.35-2.46; P < 0.001), respectively. When evaluating the prognosis of women recently diagnosed with breast cancer, the time since last full-term pregnancy should be routinely considered along with other established host and tumor prognostic factors, but consideration of other reproductive factors may not be warranted.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".