Neonatal outcomes of pregnancy‐associated breast cancer: Population‐based study on 11 million births
Bibliographic record
Abstract
BACKGROUND: As the age at first pregnancy continues to rise in the United States so does the incidence of breast cancer diagnosed during pregnancy. Our objective was to evaluate temporal trends in the incidence of pregnancy-associated breast cancer (PABC) and to measure neonatal outcomes associated with PABC. METHODS: We conducted a population-based cohort study using the 1999-2012 Healthcare Cost and Utilization Project-Nationwide Inpatient Sample (HCUP-NIS) from the United States. Logistic regression models, adjusted for maternal baseline characteristics, examined the effect of PABC on neonatal outcomes. RESULTS: There were 11 846 300 deliveries between 1999 and 2012, of which 772 cases of PABC were identified, resulting in an overall incidence of 6.5 cases/100 000 pregnancies. There was a significant increase in the incidence of PABC during the study period (P < 0.05). Women with PABC tended to be older, of white ethnicity, belong to a higher income quartile and to be treated in an urban teaching hospital. In pregnancies complicated by breast cancer, there was a greater risk of preterm delivery (OR 4.84, 95% CI 4.05-5.79) and preterm premature rupture of membranes (OR 1.79, 95% CI 1.06-3.05). No associations were observed between PABC and intrauterine growth restriction, congenital anomalies or intrauterine fetal demise. CONCLUSION: There is an uptrend in the incidence of PABC and therefore, the need for counseling these patients is also increasing. Although pregnancies with the diagnosis of maternal breast cancer are more prone to premature births, it is encouraging that these babies do not appear to be at increased risk for congenital anomalies, growth restriction, or fetal demise.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".