Behcet’s disease and pregnancy: obstetrical and neonatal outcomes in a population-based cohort of 12 million births
Bibliographic record
Abstract
Background Behcet's disease (BD) is a rare, multi-systemic inflammatory disorder for which only limited and contradictory data exists in the context of pregnancy. Our objective was to estimate the prevalence of BD in pregnancy and to evaluate maternal and fetal outcomes associated with pregnant women living with BD. Methods Using the 1999-2013 Healthcare Cost and Utilization Project-Nationwide Inpatient Sample from the United States, we performed a population-based retrospective cohort study consisting of pregnancies that occurred during this time period. ICD-9 codes were used to identify delivery admissions to women with or without BD. Multivariate logistic regression was used to estimate the adjusted effects of BD on maternal and fetal outcomes. Results Among the 12,592,676 pregnancies in our cohort, 144 were to women with BD, for an overall prevalence of 1.14 cases/100,000 births between 1999 and 2013. Over the study period, the prevalence of BD rose from 0.5 to 2.4/100,000 births. Women with BD demonstrated a two-fold greater frequency of non-delivery hospital admissions during pregnancy, and were more likely to be Caucasian, have private medical insurance, be of the upper income quartiles, and deliver at an urban teaching hospital. Women with BD were at greater risk for preterm labor and postpartum venous thromboembolism, while their newborns were more likely to be born premature. Conclusion BD-associated pregnancies are increasing in prevalence and are associated with a greater risk for adverse maternal and fetal outcomes in pregnancy. Appropriate thromboprophylaxis during pregnancy should be considered given the increased risk for venous thromboembolism.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".