Pediatric infections in the first year of life following maternal biologic exposure for autoimmune disorder treatment: A systematic review
Bibliographic record
Abstract
Pregnancy induces immunologic and physiologic changes that can alter disease activity for women with autoimmune disorders (AD), and if exacerbated, may necessitate treatment. Biologics are increasingly prescribed due to their targeted effects, but transplacental transfer to the fetus may increase potential risks to the infant. This review examines the risk of infection and respiratory distress in the first year of life among infants born to women with AD using biologics during pregnancy versus infants exposed to standard therapies. We systematically searched five databases from January 2012 to June 2023. Inclusion was restricted to cohort and case-control studies including infants born to women with rheumatoid arthritis, multiple sclerosis, or systemic lupus erythematosus prescribed a biologic or standard therapy during pregnancy. Quality assessment was performed using the ROBINS-I tool for observational studies. Due to between-study heterogeneity in effect estimates and outcomes, studies were not pooled. Of 2975 identified citations, 10 studies were included. In three studies examining the risk of infant infection, findings were inconsistent largely due to lack of precision (OR range: 0.6-1.4, 95 % CI range: 0.2-2.8). For respiratory distress, two studies reported an increased risk among infants exposed to biologics (HR 1.30, 95 % CI 1.03,1.74 and RR 1.52, 95 % CI 1.06, 2.18) while one did not. Most studies (80 %) had a moderate risk of bias. The findings suggest conflicting results for the risk of infant infection and possible associations with respiratory distress. Given the limited number of studies, additional studies are needed to inform treatment decisions for AD during pregnancy.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".