Use of disease‐modifying antirheumatic drugs during pregnancy and risk of preeclampsia
Bibliographic record
Abstract
OBJECTIVE: To describe patterns of disease-modifying antirheumatic drug (DMARD) use during pregnancy in a population-based cohort, and to evaluate the association between autoimmune disease, DMARDs, corticosteroids, and nonsteroidal antiinflammatory drugs (NSAIDs) and preeclampsia. METHODS: Using health care utilization databases from British Columbia (1997-2006), we compared the risk for preeclampsia among 44,786 women with and without autoimmune disease with study drug dispensings before pregnancy (past users) and before and during the first 20 gestational weeks (continuous users). Relative risks (RRs) and 95% confidence intervals (95% CIs) were estimated. RESULTS: Only 414 women (0.1%) had a DMARD dispensing during pregnancy. The incidence of preeclampsia was 2.3% for past DMARD users, 2.7% for past corticosteroid users, and 2.9% for past NSAID users. Compared to past users, the continuous DMARD user RR was 2.29 (95% CI 0.81-6.44), and was 0.89 (95% CI 0.51-1.56) for corticosteroid and 0.84 (95% CI 0.63-1.10) for NSAID users. Compared to women without autoimmune disease, the delivery year-adjusted RR was 2.02 (95% CI 1.11-3.64) for women with systemic lupus erythematosus (SLE). The DMARD results were attenuated when antimalarials were excluded, and the delivery year-adjusted RR was 0.95 (95% CI 0.25-3.55) when the DMARD analysis was restricted to women with autoimmune disease. CONCLUSION: Few women were exposed to DMARDs during pregnancy. We observed a 2-fold increased risk of preeclampsia among women with SLE and a nonsignificant increase in risk in DMARD users. The DMARD and preeclampsia association was attenuated when antimalarials were excluded and null when restricted to women with autoimmune disease, which suggests the association is likely due to greater autoimmune disease severity in DMARD users.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".