No Association of Discontinuing Tumor Necrosis Factor Inhibitors Before Gestational Week Twenty in Well‐Controlled Rheumatoid Arthritis and Juvenile Idiopathic Arthritis With a Disease Worsening in Late Pregnancy
Bibliographic record
Abstract
OBJECTIVE: To investigate whether the discontinuation of tumor necrosis factor inhibitors (TNFi) during pregnancy is associated with any changes of the disease course in women with rheumatoid arthritis (RA) and juvenile idiopathic arthritis (JIA). METHODS: Pregnant women with RA and JIA from the US and Canada were enrolled in the Organization of Teratology Information Specialists (OTIS) Autoimmune Diseases in Pregnancy Project, a prospective cohort study. Information about medication and disease activity (patient-reported outcome measures) was collected prior to gestational week 20 and at gestational week 32. Associations between patterns of TNFi continuation or discontinuation and disease activity changes were tested in unadjusted and multivariate analyses. RESULTS: Among 490 women (397 with RA, 93 with JIA) enrolled between 2005 and 2017, 122 (24.9%) discontinued a TNFi before gestational week 20, 201 (41.0%) received a TNFi beyond week 20, and 167 (34.1%) did not receive a TNFi during pregnancy. At the time of enrollment, disease activity was low to minimal in 72.9% of women. TNFi discontinuation was not associated with a clinically important worsening of patient reported outcome measures at the third trimester. Univariate but not multivariate analysis showed that women receiving TNFi beyond week 20 were more likely to experience improved disease activity scores at the third trimester. CONCLUSION: Discontinuing TNFi before gestational week 20 seems feasible in women with RA and JIA who enter pregnancy with well-controlled disease.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".