Sarcoidosis and Pregnancy
Bibliographic record
Abstract
INTRODUCTION: Data on sarcoidosis in pregnancy are sparse and limited to a few case reports and series. Our aim is to determine the prevalence of sarcoidosis at delivery and related maternal and newborn outcomes. METHODS: Using the U.S. Healthcare Cost and Utilization Project–Nationwide Inpatient Sample from 2003 to 2010, we conducted a population-based retrospective cohort study to compare women with and without sarcoidosis at delivery. We calculated the prevalence of sarcoidosis in pregnancy and used logistic regression analyses to estimate the associated risks of maternal and neonatal outcomes. RESULTS: There were 678 cases of sarcoidosis in 7,094,400 births over an 8-year period for an overall prevalence of 9.6 cases per 100,000 births. Compared with women in a control group, women with sarcoidosis were older, more likely to be African American, and to report being smokers. Women with sarcoidosis were more likely to have preeclampsia (odds ratio [OR] 1.62, 95% confidence interval [CI] 1.18–2.22), eclampsia (OR 5.27, 95% CI 1.69–16.40), deep vein thrombosis (OR 4.92, 95% CI 1.58–15.33), pulmonary embolism (OR 6.68, 95% CI 3.99–11.21), and premature delivery (OR 1.73, 95% CI 1.40–2.15). There was also an increased risk of caesarean deliveries and postpartum hemorrhages. There were no cases of maternal death reported. CONCLUSION: Sarcoidosis in pregnancy is a rare disease associated with an increased risk of adverse obstetric outcomes. Women with sarcoidosis can carry out successful pregnancies; however, they should be made aware of the higher risk of adverse events. Given the higher risk of venous thromboembolic events, consideration should be given to thromboprophylaxis in pregnancy.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".