Association of maternal cancer with congenital anomalies in offspring
Bibliographic record
Abstract
BACKGROUND: Congenital anomalies are common, but the possibility that maternal cancer increases the chance of having a child with a birth defect is not fully understood. OBJECTIVES: To examine the association between maternal cancer before or during pregnancy and the risk of birth defects in offspring. METHODS: We conducted a retrospective cohort study of live births in Quebec, Canada, between 1989 and 2022 using hospital data. The main exposure measure was maternal cancer before or during pregnancy. The outcome included birth defects detected in offspring during gestation or at birth. We estimated risk ratios (RR) and 95% confidence intervals (CI) for the association of maternal cancer with birth defects using log-binomial regression models adjusted for potential confounders. RESULTS: In this study of 2,568,120 newborns, birth defects were present in 6.0% and 6.7% of infants whose mothers had cancer before or during pregnancy, respectively, compared with 5.7% of infants whose mothers never had cancer. Cancer during pregnancy was associated with heart (RR 1.58, 95% CI 1.03, 2.44), nervous system (RR 4.05, 95% CI 2.20, 7.46) and urinary defects (RR 1.72, 95% CI 1.01, 2.95). Among specific types of malignancies during pregnancy, breast cancer was the most prominent risk factor for birth defects (RR 1.55, 95% CI 1.02, 2.37). Cancer before pregnancy was not associated with any type of birth defect or with defects overall (RR 1.01, 95% CI 0.92, 1.11). Moreover, no specific type of cancer before pregnancy was associated with an increased risk of birth defects. CONCLUSIONS: Maternal cancer during pregnancy is associated with the risk of congenital anomalies in offspring, however, cancer before pregnancy is not associated with this outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| 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".