Behcet’s Disease and Pregnancy: Obstetrical and Neonatal Outcomes in a Cohort of 12 Million Births [34N]
Bibliographic record
Abstract
INTRODUCTION: Behcet’s disease (BD) is a rare inflammatory disorder for which limited and contradicting data exists in the context of pregnancy. This study aimed to estimate the prevalence of BD in pregnancy and to evaluate the associated maternal and fetal outcomes. METHODS: Using data collected by the Healthcare Cost and Utilization Project Nationwide Inpatient Sample from the United States between 1999 and 2013, we performed a population-based retrospective cohort study consisting of deliveries that occurred during this time period. ICD-9 codes were used to identify deliveries underwent by women with and without BD. We calculated the prevalence of BD in pregnancy and conducted multivariate logistic regression to estimate the adjusted effects of BD on maternal and fetal outcomes. RESULTS: We found an overall prevalence of 1.14 cases per 100,000 births between 1999 and 2013, rising from 0.5 to 2.4 per 100,000 births over the 14-year study period. Women with BD demonstrated a more than two-fold greater chance of being admitted to a hospital during pregnancy (p<0.0001), and were more likely to be Caucasian, have private medical insurance, be of the third income quartile, and deliver at an urban teaching hospital. Women with BD were at increased risk for preterm labour (OR5.95, 95%CI1.47-24.12), and venous thromboembolism postpartum (OR15.22, 95%CI7.03-32.92), while their newborns were more likely to be born prematurely (OR1.73, 95%CI1.02-2.93). CONCLUSION: BD is 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 | 0.001 |
| 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".