Pregnancy Outcomes in Women with Rheumatoid Arthritis: A Retrospective Population-Based Cohort Study [7N]
Bibliographic record
Abstract
INTRODUCTION: Rheumatoid arthritis (RA) is a chronic autoimmune, inflammatory disease that is more commonly found in women. Our objective is to assess if pregnancies in women with RA are at a higher risk of adverse maternal and neonatal outcomes. METHODS: We carried out a retrospective cohort study, using the US Healthcare Cost and Utilization Project National Inpatient Sample from 2004-2013. All births were identified and women were classified as having RA or not on the basis of ICD-9 coding. Logistic regression was used to evaluate the adjusted effect of RA on maternal and neonatal outcomes. RESULTS: Of the total 8,417,607 births in our cohort, 6,068 were among women with RA. There was a steady increase in RA in pregnancy from 47 to 100/100,000 over the 10-year study period. Compared with women without RA, women with RA were more likely to experience pre-eclampsia/eclampsia (OR 1.70, 95% CI 1.54-1.89), gestational diabetes (OR 1.13, 95% CI 1.02-1.24), PPROM (OR 1.78, 95% CI 1.66-1.91), placental abruption (OR 1.43, 95% CI 1.16-1.76), placenta previa (OR 1.37, 95% CI 1.05-1.78), and to deliver by cesarean section (OR 1.38, 95% CI 1.31-1.45). Congenital anomalies (OR 2.11, 95% CI 1.65-2.72), small for gestational age (OR 2.36, 95% CI 2.09-2.66) and preterm birth (OR 1.81, 95% CI 1.67-1.95) were more common in neonates of women with RA. CONCLUSION: RA in pregnancy is associated with greater likelihood of adverse maternal and neonatal outcomes. Women with RA should be made aware of these risks and be followed as a high risk 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".