Maternal and Fetal Outcomes in Pregnancies with Long-term Corticosteroid Use: Retrospective Cohort Study [13OP]
Bibliographic record
Abstract
INTRODUCTION: Long-term corticosteroids is administered in pregnant patients with an array of autoimmune and inflammatory disorders. Our objective is to determine whether long-term corticosteroid use is associated with increased maternal and neonatal adverse outcomes. METHODS: We performed a retrospective cohort study using the Healthcare Cost and Utilization Project - National Inpatient Sample from the United States. All pregnant patients on long-term corticosteroids were identified using International Statistical Classification of Disease-9 coding from 2003 to 2015. The effect of long-term corticosteroid use on maternal and neonatal outcomes was evaluated using logistic regression. RESULTS: Out of the 10,491,798 births included in our study, 3,999 were on chronic steroids, for an overall prevalence of 38 per 100,000 births. There was a steady increase in chronic steroid use from 2 to 81 per 100,000 births over the 12-year study period ( P <.0001). Women on chronic steroids were more likely to have pregnancies complicated by gestational diabetes mellitus, 1.61 (1.46-1.79) and preeclampsia, 2.50 (2.26-2.77). They were also more likely to have caesarean sections, 1.73 (1.62-1.84), premature preterm rupture of membranes, 2.29 (2.06-2.54), sepsis, 1.77 (0.66-4.73), venous thromboembolisms, 5.25 (4.12-6.69) and uterine rupture, 3.39 (2.10-5.46). Neonates born from mothers on chronic steroids were more likely to suffer from prematurity, 3.02 (2.79-3.28) and congenital malformations, 2.42 (1.88-3.12). CONCLUSION: Long-term corticosteroids use in pregnancy is associated with maternal and fetal adverse outcomes. These patients would benefit from close follow-up throughout their pregnancy to minimize complications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".