Pregnancy, delivery and neonatal outcomes in women with gastrointestinal system cancer in pregnancy. An evaluation of a population database
Bibliographic record
Abstract
OBJECTIVES: Gastrointestinal system (GIS) cancer in pregnancy is a rare disease. Our aim was to evaluate the association between this type of cancer and pregnancy, delivery and neonatal outcomes. METHODS: We conducted a retrospective population-based cohort study using the Healthcare Cost and Utilization Project, Nation-wide Inpatient Sample (HCUP-NIS). We included all women who delivered or had a maternal death in the US between 2004 and 2014. We compared women with an ICD-9 diagnosis of GIS cancer to those without. Pregnancy, delivery, and neonatal outcomes were compared between the two groups. RESULTS: A total of 9,096,788 women met inclusion criteria. Amongst them, 194 women (2/100,000) had a diagnosis of GIS cancer during pregnancy. Women with GIS cancer, compared to those without, were more likely to be Caucasian, older than 35 years of age, and to suffer from obesity, chronic hypertension, pregestational diabetes and thyroid disease. The cancer group had a lower rate of spontaneous vaginal delivery (aOR 0.2, 95 % CI 0.13-0.27, p<0.001), and a higher rate of preterm delivery (aOR 1.85, 95 % CI 1.21-2.82, p=0.04), and of maternal complications such as blood transfusion (aOR 24.7, 95 % CI 17.11-35.66, p<0.001), disseminated intravascular coagulation (aOR 14.56, 95 % CI 3.56-59.55, p<0.001), venous thromboembolism (aOR 9.4, 95 % CI 2.3-38.42, p=0.002) and maternal death (aOR 8.02, 95 % CI 2.55-25.34, p<0.001). Neonatal outcomes were comparable between the two groups. CONCLUSIONS: Women with a diagnosis of GIS cancer in pregnancy have a higher incidence of maternal complications including maternal death, without any differences in 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".