Obstetrical and newborn outcomes among women with acute leukemias in pregnancy: a population-based study
Bibliographic record
Abstract
Purpose: Acute leukemias (ALs) are rare but aggressive malignancies. The goal of our study was to determine the incidence, obstetrical, and newborn outcomes of ALs in pregnancy.Materials and methods: We performed a retrospective population-based cohort study on all births reported in the Health-Care Cost and Utilization Project-Nationwide Inpatient Sample between 1999 and 2014. We calculated the incidence of ALs in pregnancy and conducted multivariate logistic regression to obtain adjusted odds ratios for various maternal and newborn outcomes among this population compared to a nonaffected one.Results: We identified 291 maternal cases of ALs among 14,513,587 births, yielding an incidence of 2.01 per 100,000 births over the 15-year study period. There were approximately twice as many diagnoses of acute myeloid leukemia (AML) as compared to acute lymphoid leukemia (ALL). After adjusting for differing baseline characteristics and maternal and fetal deaths, we found that pregnant women with ALs were more likely to experience post-partum hemorrhage, to suffer from disseminated intravascular coagulation (DIC), to require transfusions, to have wound complications, and to experience venous thromboembolism (VTEs). Maternal death, preterm delivery, and intrauterine fetal death (IUFD) were more common in pregnant women with ALs.Conclusion: The incidence of ALs in pregnancy appears to be greater than what was previously believed. As it is associated with several adverse maternal and fetal outcomes, affected patients should be cared for in tertiary care institutions with access to high-risk obstetrical specialists, hematologists, and neonatologists.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".