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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".