Incidence, Risk Factors, and Obstetrical Outcomes of Women with Breast Cancer in Pregnancy
Bibliographic record
Abstract
Breast cancer in pregnancy is a rare condition. The objective of our study was to describe the incidence, risk factors, and obstetrical outcomes of breast cancer in pregnancy. We conducted a population-based cohort study on 8.8 million births using data from the Healthcare Cost and Utilization Project - Nationwide Inpatient Sample from 1999-2008. The incidence of breast cancer was calculated and logistic regression analysis was used to evaluate the independent effects of demographic determinants on the diagnosis of breast cancer and to estimate the adjusted effect of breast cancer on obstetrical outcomes. There were 8,826,137 births in our cohort of which 573 cases of breast cancer were identified for an overall 10-year incidence of 6.5 cases per 100,000 births with the incidence slightly increasing over the 10-year period. Breast cancer appeared to be more common among women >35 years of age, odds ratio (OR)=3.36 (2.84-3.97); women with private insurance plans, OR=1.39 (1.10-1.76); and women who delivered in an urban teaching hospital, OR=2.10 (1.44-3.06). After adjusting for baseline characteristics, women with pregnancy-associated breast cancer were more likely to have an induction of labor, OR=2.25 (1.88, 2.70), but similar rates of gestational diabetes, preeclampsia, instrumental deliveries, and placental abruption. The incidence of breast cancer in pregnancy appears higher than previously reported with women over 35 being at greatest risk. Aside from an increased risk for induction of labor, women with breast cancer in pregnancy have similar obstetrical outcomes.
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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 | 0.005 |
| 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) | 0.000 | 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".