Management of the Pregnant Inflammatory Bowel Disease Patient on Antitumour Necrosis Factor Therapy: State of the Art and Future Directions
Bibliographic record
Abstract
Antitumour necrosis factor (anti-TNF) therapy has been a major advance in the treatment of inflammatory bowel disease (IBD) by improving rates of mucosal healing, steroid-free remission, and decreasing rates of hospitalization and surgery. Because IBD affects women in their reproductive years, clinicians have and will continue to be asked in the future about the safety profile of these agents and their potential impact on pregnancy, the developing fetus and newborn. Immunoglobulin G transfer from the mother to fetus begins in the second trimester, with an elevation starting at 22 weeks of gestation and the largest amount transferred in the third trimester. Although research investigating the long-term outcomes of children exposed to anti-TNF therapy in utero is limited, there is no known adverse effect on either pregnancy or newborn outcomes including infectious complications with this class of drugs. The World Congress of Gastroenterology consensus statement on biological therapy for IBD considered infliximab and adalimumab to be low risk and compatible with use during conception and during pregnancy in at least the first two trimesters. Based on a clinical algorithm used at the University of Calgary Pregnancy and IBD clinic (Calgary, Alberta), recommendations have been provided on the management of pregnant patients on anti-TNF therapy, particularly with regard to third-trimester dosing, taking into account disease characteristics of individual patients. When educated about the safety of anti-TNF therapy during pregnancy, patients often choose to continue on therapy during the third trimester.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| 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".