Management of Inflammatory Arthritis in pregnancy: a National Cross-Sectional Survey of Canadian rheumatologists
Bibliographic record
Abstract
BACKGROUND: With improved therapies and management, more women with inflammatory arthritides (IA) are considering pregnancy. Our objective was to survey rheumatologists across Canada about their IA management in pregnancy to identify practice patterns and knowledge gaps. METHODS: We administered an online survey with questions regarding medications for IA treatment including conventional synthetic disease modifying antirheumatic drugs (csDMARDs) and biologics/small molecules in planned and unplanned pregnancies. Email invitations were sent to members of the Canadian Rheumatology Association. We calculated responses frequencies and a priori set a cut-off of ≥75% to define consensus. RESULTS: Ninety rheumatologists participated in the survey (20% participation rate); 57% have been practicing for > 10 years, 32% for ≤10 years, and 11% in training. There was consensus on discontinuation of 4 csDMARDs - cyclophosphamide (100%), leflunomide (98%), methotrexate (96%), and mycophenolate mofetil (89%) - in planned pregnancies but varied responses on when to discontinue them or what to do in unplanned pregnancies. Respondents agreed that 3 csDMARDs - azathioprine (84%), hydroxychloroquine (95%), and sulfasalazine (77%) - were safe to continue in planned and unplanned pregnancies. There was consensus with use of 4 biologics - adalimumab (81%), certolizumab (80%), etanercept (83%), and infliximab (76%) - in planned pregnancies but uncertainty on when they should be discontinued and their use in unplanned pregnancies. CONCLUSIONS: This national survey shows consensus among rheumatologists on the use of some csDMARDs and biologics/small molecules in IA patients planning pregnancy but varied knowledge on when to discontinue and what to do in unplanned pregnancies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".