PREGNANCY OUTCOMES AFTER EXPOSURE TO TNF-α INHIBITORS FOR THE TREATMENT OF ARTHRITIC DISEASES: A META-ANALYSIS OF OBSERVATIONAL STUDIES
Bibliographic record
Abstract
TITLE: Pregnancy Outcomes after Exposure to TNF-α Inhibitors During Pregnancy for the Treatment of Arthritic Diseases: A Meta-Analysis Authors: Mirdamadi K, Salinas T, Vali R, Papadimitripulous M, Piquette-Miller M Background: Auto-immune arthritic diseases affect many women of child-bearing age. Tumor necrosis factor (TNF)-α inhibitors are currently used for the treatment of various immune-mediated diseases during pregnancy. However, there has been no evaluation of safety in the treatment of arthritic diseases during gestation. Objective: To analyze the risk of adverse pregnancy and neonatal outcomes after treatment of arthritic diseases with TNF-α inhibitors. Methods: Major databases including Ovid MEDLINE, Embase, and Web of Science, were searched inclusive to April 2016. Observational prospective cohort studies evaluating pregnancy outcomes after exposure to TNF-α inhibitors for the treatment of arthritic diseases during pregnancy were included. Data on pregnancy and neonatal outcomes was extracted from all included studies. A meta-analysis was performed using inverse-variance random effect with a 95% confidence interval (95%CI) and p<0.05. Results: Eight prospective studies with comparison groups were included in the meta-analysis. TNF-α inhibitors were associated with significantly higher risks of low birth weight (odds ratio (OR), 1.43; 95%CI, 1.00-2.04) and significantly lower rates of live birth (OR, 0.61; 95%CI, 0.38-0.98). However, birth defects, therapeutic abortion, spontaneous abortion, and preterm birth were not significantly different between the two groups. Conclusion: Treatment of arthritic diseases with TNF-α inhibitors during pregnancy increases the risk of lower birth weight and decreases the rate of live birth in this population. While duration of treatment and gestational age at exposure may play a role in these outcomes, evaluation of risk versus benefit is crucial in this patient population. Key words: TNF-α inhibitors, pregnancy outcomes, arthritic disease, meta-analysis.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.060 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".