Maternal and neonatal outcomes of pregnancies in women with Addison's disease: a population‐based cohort study on 7.7 million births
Bibliographic record
Abstract
OBJECTIVE: To assess if pregnancies among women with Addison's disease (AD) are at higher risk of adverse maternal and neonatal outcomes. DESIGN: Population-based retrospective cohort study. SETTING/POPULATION: All births in the United States' Healthcare Cost and Utilization Project-Nationwide Inpatient Sample from 2003 to 2011. METHODS: Baseline characteristics were compared between women with AD and those without, and prevalence over time was measured. Logistic regression was used to estimate the effect of AD on maternal and neonatal outcomes by calculating the crude and adjusted odds ratios (OR) and corresponding 95% confidence intervals (95% CI). RESULTS: We calculated a prevalence of AD in pregnancy of 5.5/100 000, increasing from 5.6 to 9.6/100 000 (P = 0.0001) over the 9-year study period. Compared with women without AD, women with AD were more likely to deliver preterm (OR 1.50, 95% CI 1.16-1.95), deliver by caesarean section (OR 1.32, 95% CI 1.08-1.61), have impaired wound healing (OR 4.28, 95% CI 2.55-7.18), develop infections (OR 2.44, 95% CI 1.66-3.58) and develop thromboembolism (OR 5.21, 95% CI 2.15-12.63), require transfusions (OR 6.69, 95% CI 4.69-9.54), and have prolonged postpartum hospital admissions (OR 5.71, 95% CI 4.37-7.47). Maternal mortality was significantly higher than in the comparison group (OR 22.30, 95% CI 6.82-72.96). Congenital anomalies (OR 3.62, 95% CI 2.05-6.39) and small-for-gestational age infants (OR 1.78, 95% CI 1.15-2.75) were more likely in these pregnancies. CONCLUSIONS: Addison's disease significantly increases the risk of severe adverse maternal and neonatal outcomes, so pregnant women with AD are best managed in tertiary-care centres. TWEETABLE ABSTRACT: Pregnancies complicated by Addison's disease have an increased risk of adverse maternal and neonatal outcomes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".