004 Pregnancy-related relapse in natalizumab, fingolimod and dimethyl fumarate-treated women with multiple sclerosis
Bibliographic record
Abstract
Objective To investigate pregnancy-related disease activity in a contemporary multiple sclerosis (MS) cohort. Methods Data were obtained from the MSBase Registry. Term/preterm pregnancies conceived from 2011-2019 were included (modern cohort). Annualised relapse rates (ARR) were calculated before, during and after pregnancy. Predictors of intrapartum and early postpartum (1st3 months) relapse were determined by clustered logistic and Cox regression analyses, respectively. Results We included 1640 pregnancies from 1452 women. Disease-modifying therapy (DMT) used in the one-year preconception included natalizumab (n=219), fingolimod (n=147), dimethyl fumarate (DMF; n=57) and low-efficacy therapies (n=845). Preconception ARR by DMT class used before conception were: natalizumab, 0.29 (95% CI 0.22-0.37); fingolimod, 0.37 (0.28-0.49); DMF, 0.24 (0.13-0.41); low-efficacy, 0.29 (0.25-0.33); and none, 0.24 (0.19-0.31). Among women who used fingolimod or natalizumab, ARR increased during pregnancy. Intrapartum ARR decreased in preconception DMF, low-efficacy or no DMT groups. ARR spiked after delivery across all DMT groups. Natalizumab continuation into pregnancy reduced the odds of relapse during pregnancy (OR 0.76 per month [0.60-0.95], p=0.017). DMT re-initiation with natalizumab protected against postpartum relapse (HR 0.11 [0.04-0.32], p<0.0001). Breastfeeding women were less likely to relapse (HR 0.61 [0.41-0.91], p=0.016). Conclusion Women with MS prescribed natalizumab or fingolimod preconception had higher rates of intrapartum and postpartum relapse. In women considered to be at high relapse risk, use of natalizumab before pregnancy and continued up to 32-34 weeks gestation, with early re-initiation after delivery is an effective option to minimise relapse risks. Strategies of DMT use have to be balanced against potential foetal/neonatal complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".