Serious Infections in Offspring Exposed to Tumor Necrosis Factor Inhibitors During Pregnancy: Comparison of Timing During Pregnancy and Placental Transfer Ability
Bibliographic record
Abstract
OBJECTIVE: We evaluated serious infection risk in offspring exposed to tumor necrosis factor inhibitors (TNFi) in utero, separated by TNFi timing and placental transfer ability. METHODS: Using MarketScan (2011-2021), we identified offspring born to mothers with chronic inflammatory diseases. TNFi exposure was defined as at least one filled prescription during pregnancy, further subdivided by trimesters and placental transfer. The event of interest was the time to first hospitalization with infection in the offspring's first year of life. We estimated associations between TNFi exposure and infection risk using multivariable Cox proportional hazards models, adjusting for maternal demographics, disease type, comorbidities, pregnancy complications, and drug exposure. RESULTS: We identified 56,866 offspring; 3,711 (6.5%) were exposed to TNFi during pregnancy. Overall, the association of TNFi exposure with the risk of serious infections vs unexposed offspring was not statistically significant (hazard ratio [HR] 0.85; 95% confidence interval [CI] 0.68-1.07). However, offspring exposed during the third trimester had a 70% higher risk of serious infections than those exposed only in the first and/or second trimesters (HR 1.70; 95% CI 0.96-3.01). Additionally, we observed an increased risk with exposure to any TNFi with high placental transfer ability (infliximab, adalimumab, golimumab) overall (HR 1.49; 95% CI 0.83-2.69) and during the third trimester (HR 1.30, 95% 0.65-2.61), compared to only low placental transfer TNFi (certolizumab, etanercept), though both HR were not statistically significant. CONCLUSION: Overall, TNFi exposure was not associated with serious infections; exploratory signals by timing and placental transfer were imprecise and require confirmation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".