Tumour necrosis factor inhibitors and serious infections in reproductive-age women and their offspring: a narrative review
Bibliographic record
Abstract
Tumour necrosis factor inhibitors (TNFi) are commonly used to treat patients with chronic inflammatory diseases, and function by inhibiting the pro-inflammatory cytokine tumour necrosis factor-α (TNF-α). Although beneficial in reducing disease activity, they are associated with an increased risk of serious infections. Data on the risk of serious infections associated with TNFi use during the reproductive years, particularly in pregnancy, are limited. For pregnant women, there is an additional risk of immunosuppression in the offspring as TNFi can be actively transported across the placenta, which increases in the second and third trimesters. Several studies have explored the risk of serious infections with TNFi exposure in non-pregnant and pregnant patients and offspring exposed in utero, indicating an increased risk in non-pregnant patients and a potentially increased risk in pregnant patients. The studies on TNFi-exposed offspring showed conflicting results between in utero TNFi exposure and serious infections during the offspring's first year. Further research is needed to understand differential risks based on TNFi subtypes. Guidelines conditionally recommend the rotavirus vaccine before 6 months of age for offspring exposed to TNFi in utero, but more data are needed to support these recommendations because of limited evidence. This narrative review provides an overview of the risk in non-pregnant patients and summarizes evidence on how pregnancy can increase vulnerability to certain infections and how TNFi may influence this susceptibility. This review focuses on the evidence regarding the risk of serious infections in pregnant patients exposed to TNFi and the risk of infections in their offspring.
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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 | 0.002 | 0.002 |
| 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) | 0.004 | 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".