Normal Infant Immunologic Assessment and Uneventful Live Rotavirus Vaccination Despite Continuous Tofacitinib Exposure In Utero and During Breastfeeding
Bibliographic record
Abstract
Background: Janus kinase (JAK) inhibitors are effective for the treatment of inflammatory bowel disease (IBD). However, this class of medications is not recommended during pregnancy or breastfeeding based on animal data suggesting teratogenesis and recent reports of transmammary transfer after maternal ingestion, raising concerns for immune system development in babies exposed to these drugs. Methods: We present the case of a patient with IBD treated with a JAK inhibitor who decided to continue the medication throughout her pregnancy and during breastfeeding. This is the first reported case of a detailed immunologic profile in a baby exposed to tofacitinib in utero and during lactation. Results: A 30-year-old female with ulcerative colitis with previous exposure to vedolizumab and infliximab achieved complete remission with tofacitinib therapy. The patient became pregnant after 5 months of JAK inhibitor therapy and decided to continue tofacitinib during pregnancy and while breastfeeding. The patient delivered a healthy offspring with no congenital malformations, a normal detailed immunologic profile, and subsequent safe provision of the live oral rotavirus vaccine. Conclusions: This case highlights the importance of individualized counseling for patients of childbearing age who are candidates for JAK inhibition. Those who are pregnant or breastfeeding with refractory disease may have limited medical therapeutic options. Ongoing effective therapy for IBD resulted in complete disease remission in the mother and favorable outcomes in the infant. Further, an in-depth infant immunological assessment can lead to specific vaccination recommendations in exposed infants.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".