Serious Infections in Rheumatoid Arthritis Offspring Exposed to Tumor Necrosis Factor Inhibitors
Bibliographic record
Abstract
OBJECTIVE: To evaluate the risk of serious infections in rheumatoid arthritis (RA) offspring exposed to tumor necrosis factor inhibitors (TNFi) in the gestational period compared to unexposed RA offspring, as well as to children from the general population. METHODS: We used US claim data (2011-2015) to identify 2,989 offspring born to women who have RA and a randomly selected group of 14,596 control children, matched ≥4:1 for maternal age, year of delivery, and state of residence. We defined TNFi exposure based on ≥1 filled prescription during pregnancy. We ascertained serious infections based on ≥1 hospitalization, with infection as a primary diagnosis, at ≤12 months of life. We performed multivariable analyses, adjusting for maternal demographics, comorbidities, pregnancy complications, and drugs. RESULTS: Among RA offspring, 380 (12.7%) were exposed to TNFi during pregnancy. The percentage of serious infections in RA offspring with no TNFi exposure (2.0%; 95% confidence interval [95% CI] 1.5, 2.6) was similar to that in non-RA offspring (1.9%; 95% CI 1.9, 2.2), while the percentage of serious infections in RA offspring with TNFi exposure was 3.2% (95% CI 1.5, 5.6). In multivariable analyses, we were unable to establish an increased risk of serious infections in RA offspring exposed to TNFi versus both non-RA offspring (odds ratio [OR] 1.7, 95% CI 0.8, 3.7) and RA offspring unexposed to TNFi (OR 1.4, 95% CI 0.7, 2.8). CONCLUSION: We did not demonstrate a marked excess risk for serious infections in RA offspring exposed to TNFi during pregnancy versus unexposed RA offspring or general population controls.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".