Disease Severity and Pregnancy Outcomes in Women with Rheumatoid Arthritis: Results from the Organization of Teratology Information Specialists Autoimmune Diseases in Pregnancy Project
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of rheumatoid arthritis (RA) disease severity on pregnancy outcomes in pregnant women with and without autoimmune diseases. METHODS: A prospective cohort study was conducted using the Organization of Teratology Information Specialists Autoimmune Diseases in Pregnancy Project. Pregnant women with RA enrolled between 2005 and 2013 were selected if they (1) delivered a live-born singleton infant; and (2) completed 3 telephone-based measures of RA disease severity prior to 20 weeks' gestation, including the Health Assessment Questionnaire Disability Index (HAQ-DI), pain score, and patient's global scale. Associations between RA disease severity and preterm delivery, small for gestational age (SGA), or cesarean delivery were tested in unadjusted and multivariate analyses using modified Poisson regression models. RESULTS: The sample consisted of 440 women with RA. Several unadjusted comparisons yielded significant associations. After adjustment for covariates, increasing disease severity was associated with risk for preterm delivery and SGA. For each unit increase in HAQ-DI (0-1), the adjusted relative risk (aRR) for preterm delivery increased by 58% (aRR 1.58, 95% CI 1.17-2.15). Among those with HAQ-DI > 0.5, the aRR for SGA was 1.81 (95% CI 1.01-3.33). CONCLUSION: RA disease severity in early pregnancy, as measured in this study, was predictive of preterm delivery and SGA. These findings suggest that the risk of preterm delivery and SGA in women with RA might be lowered if RA is well controlled early 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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".