Disease-modifying anti-rheumatic drug use in pregnant women with rheumatic diseases: a systematic review of the risk of congenital malformations.
Bibliographic record
Abstract
OBJECTIVES: Despite the high incidence of rheumatic diseases during the reproductive years, little is known about the impact of disease-modifying anti-rheumatic drug (DMARD) use during pregnancy. Our objective was to systematically review and appraise evidence in women with rheumatic disease on the use of traditional and biologic DMARDs during pregnancy and the risk of congenital malformation outcomes. METHODS: We conducted a systematic search of MEDLINE, EMBASE, and INTERNATIONAL PHARMACEUTICAL ABSTRACTS databases. Inclusion criteria were: 1) study sample including women with rheumatic disease; 2) use of traditional and/or biologic DMARDs during pregnancy; and 3) congenital malformation outcome(s) reported. We extracted information on study design, data source, number of exposed pregnancies, type of DMARD, number of live births, and number of congenital malformations. RESULTS: Altogether, we included 79 studies; the majority were based on designs that did not involve a comparison group, including 26 case reports, 17 case series, 20 cross-sectional studies, and 4 surveys. Studies that had a comparator group included 1 case control, 10 cohort studies, and 1 controlled trial. Hydroxychloroquine and azathioprine represent the most studied traditional DMARD exposures and, among biologics, most of the reports were on infliximab and etanercept. CONCLUSIONS: This is the first systematic review on the use of both traditional and biologic DMARDs during pregnancy among women with rheumatic diseases and congenital malformation outcomes, with a focus on study design and quality. Findings confirm the limited number of studies, as well as the need to improve study designs.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".