Four years on: Pregnancy and birth outcomes reported in the MSBase pregnancy, neonatal outcomes, and Women’s Health Registry (2020–2024)
Bibliographic record
Abstract
BACKGROUND: Family planning is an important aspect of multiple sclerosis (MS), and neuromyelitis optica spectrum disorder (NMOSD) management. Knowledge gaps remain, including optimal perinatal management strategies, and fetal risks associated with disease-modifying therapy (DMT) exposure. OBJECTIVE: To describe perinatal DMT use, together with pregnancy and neonatal outcomes prospectively recorded in the International MSBase Pregnancy and Women's Health Registry. METHODS: We report summary statistics for data collected between May 2020 and August 2024. RESULTS: A total of 1887 relapsing-remitting MS (RRMS), 12 primary-progressive MS (PPMS), 2 radiologically isolated syndrome (RIS) and 21 NMOSD completed pregnancies were recorded, including 1644 (85.5%) live births, 208 (10.8%) miscarriages, and 6 (0.3%) neonatal deaths. Most women had unassisted (53.8%) or assisted (7.4%) vaginal births. Seventy five percent of pregnancies had DMT exposures within 6 months preconception; 19% of NMOSD, and 62% of MS pregnancies were DMT-exposed during gestation; 18.1% of pregnancies reported in-pregnancy monoclonal antibody DMT exposure. No overt safety signals were seen. CONCLUSION: This first report from the newly launched MSBase pregnancy registry, establishes an increasing number of pregnancies being conceived on monoclonal antibody therapies. Although no safety signals were observed, it is important to continue monitoring for safety signals in real-world databases as the use of highly effective therapies continues to increase perinatally.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".