From mother to baby: antenatal exposure to monoclonal antibody biologics
Bibliographic record
Abstract
INTRODUCTION: More women with autoimmune and inflammatory conditions are being treated with monoclonal antibody biologics (mAbs) during their pregnancy, to maintain clinical remission. The use of anti-tumor necrosis factor alpha agents in pregnancy appears to be safe but less is known regarding other mAbs, such as anti-integrins and anti-cytokine agents. There are currently no comprehensive guidelines on how to manage the exposed infants. Areas covered: We review recent literature to assess the impact of mAbs on birth and early infant outcomes, including what is currently known about maternal and infant drug levels at birth and drug clearance in the infant. We describe the potential risks of infections and reported hematological and immunological effects of antenatal mAbs exposure on the infant and provide guidance on the management of the exposed infant. Expert opinion: Exposed infants should be monitored closely. Certain mAb exposures require specific testing and management. Safety monitoring should be done in a multidisciplinary approach and should include pediatric care providers. The current clinical experience with anti-tumor necrosis factor agents in pregnancy cannot be extrapolated to other mAbs. Long-term observational studies and a multicenter international registry are needed to better appreciate the impact of exposure, especially to newer mAbs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".