Antirheumatic medication use among pregnant women with rheumatoid arthritis: a Norwegian nationwide drug utilisation study
Bibliographic record
Abstract
Objectives: Managing rheumatoid arthritis (RA) during pregnancy requires balancing maternal health and foetal safety. This study examined the prevalence, temporal trends, utilisation patterns, and concurrent use of antirheumatic medications from preconception through the postpartum period. Methods: We conducted a nationwide study using Norwegian registry data (2006-2018), linking the medical birth registry with prescription and primary care records. Pregnant women with RA were identified through diagnostic codes. Dispensed prescriptions were evaluated from 9 months before the last menstrual period to 9 months after delivery. Multitrajectory models identified distinct patterns of antirheumatic medication use over time. Results: Of 769,524 pregnancies, 3732 (0.5%) occurred in women with RA. In the period around pregnancy, 52.9% used non-steroidal anti-inflammatory drugs (NSAIDs), 33.6% glucocorticoids, 17.7% pregnancy-compatible conventional synthetic disease-modifying antirheumatic drugs (csDMARDs), 14.8% pregnancy-contraindicated csDMARDs, and 21.6% tumour necrosis factor (TNF) inhibitors. Medication use declined from 61.3% preconceptionally to 37.6%, 22.1%, and 18.9% in the first, second, and third trimesters, respectively, then increased to 53.0% postpartum. From 2008 to 2018, antirheumatic medication use increased preconceptionally (59.8%-65.7%), during pregnancy (30.6%-49.5%), and postpartum (45.1%-54.1%), mainly due to increased TNF inhibitor use. Three distinct medication use patterns emerged: (i) NSAID-dominant use prepregnancy, (ii) glucocorticoid plus pregnancy-compatible csDMARD use continuing into pregnancy; and (iii) pregnancy-contraindicated csDMARD interrupters, mostly discontinuing during pregnancy and restarting postpartum. Conclusions: Although antirheumatic medication use increased over time, most women discontinued treatment during pregnancy and resumed it after childbirth. The 3 distinct usage patterns highlight the need for individualised counselling and close monitoring to optimise disease management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".