Biologicals in atopic disease in pregnancy: An EAACI position paper
Bibliographic record
Abstract
Biologicals have transformed the management of severe disease phenotypes in asthma, atopic dermatitis, and chronic spontaneous urticaria. As a result, the number of approved biologicals for the treatment of atopic diseases is continuously increasing. Although atopic diseases are among the most common diseases in the reproductive age, investigations, and information on half-life, pharmacokinetics defining the neonatal Fc receptors (FcRn) and most important safety of biologicals in pregnancy are lacking. Given the complex sequence of immunological events that regulate conception, fetal development, and the intrauterine and postnatal maturation of the immune system, this information is of utmost importance. We conducted a systematic review on biologicals in pregnancy for indications of atopic diseases. Evidence in this field is scarce and mainly reserved to reports on the usage of omalizumab. This lack of evidence demands the establishment of a multidisciplinary approach for the management of pregnant women who receive biologicals and multicenter registries for long-term follow-up, drug trial designs suitable for women in the reproductive age, and better experimental models that represent the human situation. Due to the very long half-life of biologicals, preconception counseling and healthcare provider education are crucial to offer the best care for mother and fetus. This position paper integrates available data on safety of biologicals during pregnancy in atopic diseases via a systematic review with a detailed review on immunological considerations how inhibition of different pathways may impact 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.007 | 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".