Effect of Sjögren’s syndrome on maternal and neonatal outcomes of pregnancy
Bibliographic record
Abstract
Background Sjögren's syndrome (SS) is an autoimmune connective tissue disease affecting the body's moisture-producing glands. Some studies have linked SS to adverse maternal/neonatal outcomes, but sample sizes have tended to be small, with few outcomes examined. The purpose of this study was to evaluate the effect of SS on pregnancy outcomes for mother and neonate using a large dataset. Methods We carried out a retrospective cohort study of women who delivered between 1999 and 2014 using data from the Nationwide Inpatient Sample from the United States. SS categorization is based on ICD-9 coding. Baseline characteristics were compared in both groups and multivariate logistic regression was used to compare maternal and fetal outcomes of pregnancies in women with and without SS. Results The prevalence of SS in our population was 1.34 cases/10,000 births, with the rate increasing over the study period. Women with SS tended to be older, Caucasian and to have pre-existing comorbidities. Births to women with SS were at greater risk of pre-eclampsia [odds ratio (OR) 1.63, 95% confidence interval (CI) 1.34-1.99]; premature rupture of membranes (OR 1.28, 95% CI 1.04-1.57); preterm delivery (OR 1.56, 95% CI 1.34-1.81); cesarean delivery (OR 1.29, 95% CI 1.17-1.41); and venous thromboembolic events (OR 3.71, 95% CI 2.57-5.35). Infants of women with SS were more likely to have intrauterine growth restriction (IUGR) (OR 3.00, 95% CI 2.46-3.65); and congenital malformations (OR 3.26, 95% CI 2.30-4.62). Conclusion SS is a high-risk pregnancy condition associated with significant comorbidities and adverse maternal and fetal outcomes. Women with SS may benefit from increased surveillance during their pregnancies.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".