Relationship of Time Since Childbirth and Other Pregnancy Factors to Premenopausal Breast Cancer Prognosis
Bibliographic record
Abstract
OBJECTIVE: To investigate the influence of time since childbirth and other pregnancy factors on the prognosis of premenopausal breast cancer. METHODS: Women who delivered an infant in Nova Scotia, Canada, between 1980 and 2001 were identified from a provincial perinatal database and linked to the Nova Scotia Cancer Registry to determine primary breast cancer diagnoses among women aged younger than 50 years. Relative risks and Cox proportional hazards ratios were calculated to quantify the relationship of time from childbirth to diagnosis and other pregnancy factors to the extent of disease at diagnosis and on survival after breast cancer diagnosis. RESULTS: Of the 123,323 women who delivered an infant during the study period, 716 women were diagnosed with invasive breast cancer. Women with less than 5 years between their last delivery and diagnosis were more likely to be diagnosed with later-stage disease and had poorer survival even after adjusting for stage of disease (less than 2 years, adjusted hazards ratio 2.1, 95% confidence interval 1.2-3.9; 2-4 years, hazards ratio 1.6, 95% confidence interval 0.9-2.8) compared with women with 5 years or more. For every 13 women with less than 2 years between delivery and diagnosis, one excess death will occur, compared with women with 5 or more years between delivery and diagnosis. CONCLUSION: A time interval of less than 2 years (and 2-4 years) between childbirth and breast cancer diagnosis worsens the prognosis in a dose-response fashion. Clinicians should be aware of these findings when examining women in the first 5 years after a delivery. LEVEL OF EVIDENCE: II.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".