Perinatal Use and Discontinuation of Disease-Modifying Antirheumatic Drugs
Bibliographic record
Abstract
BACKGROUND: Managing rheumatic disease activity using pregnancy-compatible medications is essential for reducing adverse maternal and fetal outcomes. We characterized medication use and discontinuation before, during, and after pregnancy, among female patients with rheumatic diseases attending a targeted pregnancy and rheumatic diseases clinic. METHODS: We conducted a cross-sectional medical record review of female patients with rheumatic diseases at a Canadian clinic between January 2017 and July 2020. Patients were categorized by pregnancy stage at their latest clinic visit: (1) preconception; (2) pregnant; (3) postpartum. We assessed use of conventional, biologic, and targeted synthetic disease-modifying antirheumatic drugs (DMARDs), prednisone, and nonsteroidal anti-inflammatory drugs across 6 perinatal windows: 24 and 12 months preconception, each pregnancy trimester, and 3 months postpartum. We reported adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for medication discontinuation in the first trimester and subsequent disease flare. RESULTS: Of 230 included patients, 85 (37.0%), 12 (5.2%), and 133 (57.8%) were preconception, pregnant, and postpartum, respectively. Approximately half experienced at least 1 disease flare during each pregnancy stage (56.4% preconception, 58.1% during pregnancy, and 53.7% postpartum). Most used at least 1 DMARD throughout the perinatal period (82.6% preconception, 55.6% during pregnancy, and 45.1% postpartum). Overall, 25.5% discontinued at least 1 DMARD in the first trimester. DMARD discontinuation was associated with disease flare during pregnancy (aOR, 1.49; 95% CI, 0.55-4.03; p = 0.87) and postpartum (aOR, 3.09; 95% CI, 0.83-11.47; p = 0.09). CONCLUSIONS: Patients receiving care at a pregnancy and rheumatic disease clinic show perinatal medication use patterns consistent with recent recommendations and clinical guidelines.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".