Maternal and neonatal outcomes associated with biologic exposure before and during pregnancy in women with inflammatory systemic diseases: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
OBJECTIVE: To determine the association between exposure to biologics in pregnant women with inflammatory systemic diseases and maternal and neonatal outcomes through a meta-analysis of findings from studies identified in a systematic review. METHODS: We conducted a systematic review of Medline, Embase, and Cochrane Database of Systematic Reviews to identify observational studies assessing the perinatal impacts of biologic in women with inflammatory systemic disease. Findings were meta-analysed across included studies with random-effects models. Crude risk estimates and, where possible, adjusted risk estimates were pooled to determine the impact on results when confounding is addressed. RESULTS: Overall, 24 studies were included in the meta-analysis. Meta-analyses of crude risk estimates resulted in pooled odds ratios (OR) for the association of biologic use during pregnancy and the following respective outcomes: congenital anomalies (1.30, 95% CI: 1.02, 1.67), preterm birth (OR 1.61, 95% CI: 1.37, 1.89), and low birth weight (OR 1.68, 95% CI: 1.21, 2.31). However, in pooled analyses of adjusted risk estimates we observed that the association between biologics use during pregnancy in disease-matched exposed and unexposed pregnant women was no longer statistically significant for congenital anomalies (adjusted OR 1.18, 95% CI: 0.88, 1.57). CONCLUSION: Pooled results from studies reporting adjusted risk estimates showed no increased risk of congenital anomalies associated with biologics use, suggesting that increased rates of adverse outcomes may be due to disease activity itself or other confounders.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.047 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".