Cancer chemotherapy in pregnancy and adverse pediatric outcomes: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Administration of chemotherapy during pregnancy is often delayed, while preterm delivery is common. If in utero exposure to chemotherapy is associated with adverse pediatric outcomes, it is unknown whether that relationship is directly attributable to the chemotherapy or is mediated by preterm birth. METHODS: Patients were identified from Canadian cancer registries and administrative data in Alberta, British Columbia, and Ontario, 2003-2017, with follow-up until 2018. The primary exposure was receipt of chemotherapy during pregnancy. Severe neonatal morbidity and mortality (SNM-M), neurodevelopmental disorders and disabilities (NDDs), and pediatric complex chronic conditions (PCCC) reflected short- and long-term pediatric outcomes. Modified Poisson and Cox proportional hazard regression models generated adjusted risk ratios (RR) and hazard ratios (HR), respectively. The influence of preterm birth on the association between exposure to chemotherapy in pregnancy and each study outcome was explored using mediation analysis. RESULTS: Of the 1150 incident cases of cancer during pregnancy, 142 (12.3%) received chemotherapy during pregnancy. Exposure to chemotherapy in pregnancy was associated with a higher risk of SNM-M (RR = 1.67, 95% confidence interval [CI] = 1.13 to 2.46), but not NDD (HR = 0.93, 95% CI = 0.71 to 1.22) or PCCC (HR = 0.96, 95% CI = 0.80 to 1.16). Preterm birth less than 34 and less than 37 weeks mediated 75.8% and 100% of the observed association between chemotherapy and SNM-M, respectively. CONCLUSIONS: Most children born to people with cancer during pregnancy appear to have favorable long-term outcomes, even after exposure to chemotherapy in pregnancy. However, preterm birth is quite common and may contribute to increased rates of adverse neonatal 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".