Pregnancy Outcomes in Women With Multiple Sclerosis
Bibliographic record
Abstract
Few studies have assessed the risk of adverse pregnancy outcomes in women with multiple sclerosis (MS). We used 2 large US administrative databases, the Truven Health MarketScan Database (2011-2015; Truven Health Analytics Inc., Ann Arbor, Michigan) and the Nationwide Inpatient Sample (2007-2011), to identify delivery cohorts. MS and pregnancy outcomes (infections, cesarean delivery, preterm delivery, poor fetal growth, preeclampsia, chorioamnionitis, postpartum hemorrhage, stillbirth, and infant malformations) were identified during pregnancy and at delivery. We calculated adjusted risk ratios according to MS status and relapse(s) in the year before delivery. Among over 5 million pregnancies, we identified 3,875 pregnancies in women with MS. Women with MS had an increased risk of infections during pregnancy (Truven Health: adjusted risk ratio (aRR) = 1.22, 95% confidence interval (CI): 1.16, 1.27) and preterm delivery (Truven Health: aRR = 1.19 (95% CI: 1.04, 1.35); Nationwide Inpatient Sample: aRR = 1.30 (95% CI: 1.16, 1.44)). The risks of other outcomes were similar for women with and without MS. In the Truven Health database, risk ratios for the pregnancy outcomes in women experiencing relapses versus those without relapses were between 0.9 and 1.4, and confidence intervals overlapped the null. Overall, women with MS had an increased risk of infections and preterm delivery; however, their risks for other adverse pregnancy outcomes were not elevated. Disease activity before delivery was not a strong predictor of outcomes.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".