Pregnancy Outcomes of Targeted Synthetic Disease‐Modifying Antirheumatic Drugs Among Patients With Autoimmune Diseases: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: Targeted synthetic disease-modifying antirheumatic drugs (tsDMARDs) have expanded the management of autoimmune diseases, including rheumatic diseases. As the use of these drugs grows, it is important to understand their effects on pregnancy. We conducted a scoping review to synthesize the current evidence on the impacts of tsDMARDs on pregnancy outcomes. METHODS: We searched the Embase, MEDLINE, and CENTRAL databases in November 2023. We included studies that examined tsDMARD exposure for chronic autoimmune disease(s), particularly in mothers during pregnancy, fathers before conception, and/or fetuses/neonates in utero. We extracted data on sample size, study design, tsDMARD exposure (dose and duration), and reproductive health outcomes. RESULTS: Of 6,712 studies screened, eight were included, namely nine case reports, one case series, four cross-sectional studies, and one cohort study among patients with ulcerative colitis, rheumatoid arthritis, and psoriasis. Sample sizes ranged from 1 to 116 pregnancies or offspring, with six studies on tofacitinib, one on baricitinib, one on upadacitinib, and no studies on apremilast. Overall, 19 fetal/neonatal outcomes, six fetal/neonatal-maternal outcomes, and three maternal outcomes were extracted. The most frequently reported fetal/neonatal outcomes were congenital anomaly (n = 4), preterm birth (n = 4), and the fetal/neonatal-maternal outcome of spontaneous abortion (n = 4). Only one study reported on the maternal outcome of delivery via Cesarean section. CONCLUSION: Our scoping review of evidence to date on the perinatal use of tsDMARDs reveal small sample sizes and a limited number of studies, all largely descriptive in nature. Findings highlight evidence gaps that preclude providers and patients from making informed decisions when considering the perinatal use of tsDMARDs.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".