Perinatal exposure to conventional synthetic disease-modifying anti-rheumatic drugs in women with rheumatic disease and neonatal outcomes: a population-based study.
Bibliographic record
Abstract
OBJECTIVES: Epidemiologic studies evaluating associations between specific arthritis medications and perinatal outcomes are limited. We evaluated the association between conventional synthetic DMARD (csDMARD) use among women with rheumatic disease (RD) and neonatal outcomes. METHODS: We linked population-based data in British Columbia, Canada from 01/01/2002 to 12/31/2012 on all inpatient/outpatient visits and medications with a perinatal registry. For small-for-gestational-age (SGA) births, we assessed csDMARD exposure 90 days preconception or during pregnancy until date of delivery. For congenital anomalies, we determined csDMARD exposure 90 days preconception or during the first trimester. We used multivariable logistic regression models fitted with generalised estimating equations and calculated post-hoc power. RESULTS: There were 185 pregnancies in 175 women (31.3±5.4 years) and 6,064 pregnancies in 4,387 women (31.1±5.4 years) in the csDMARD exposed and unexposed groups, respectively. Hydroxychloroquine, azathioprine, sulfasalazine, and methotrexate exposure before or during pregnancy were not associated with SGA births. The most sufficiently powered analyses were those for hydroxychloroquine, where exposure during pregnancy resulted in an adjusted odds ratio (aOR) of 1.12 (95% confidence interval [CI], 0.65-1.94) for SGA births. Although post-hoc power calculations indicate less power to detect associations between csDMARDs and congenital anomalies, results indicate methotrexate exposure during the first trimester is associated with elevated odds for congenital anomalies (aOR 6.58, 95% CI 1.15-37.75). CONCLUSIONS: Findings are consistent with current guidelines regarding specific csDMARD use during the perinatal period for women with RD. It is important to report well-designed epidemiologic studies to facilitate future RD/csDMARD-specific meta-analyses.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".