Management of Multiple Sclerosis During Pregnancy and the Reproductive Years
Bibliographic record
Abstract
OBJECTIVE: To examine the evidence guiding management of multiple sclerosis (MS) in reproductive-aged women. DATA SOURCES: We conducted an electronic literature search using PubMed, ClinicalTrials.gov, and other available resources. The following keywords were used: "multiple sclerosis" and "pregnancy." We manually searched the reference lists of identified studies. METHODS OF STUDY SELECTION: Two reviewers categorized all studies identified in the search by management topic, including effect of pregnancy on MS course, fetal risks associated with disease-modifying treatments during pregnancy, and management of patients off disease-modifying treatment. We categorized studies by strength of evidence and included prior meta-analyses and systematic studies. These studies were then summarized and discussed by an expert multidisciplinary team. TABULATION, INTEGRATION, AND RESULTS: The risk of MS relapses is decreased during pregnancy and increased postpartum. Data are lacking regarding the risks of disease-modifying treatments during pregnancy. There may be an increased risk of MS relapses after use of assisted reproductive techniques. There does not appear to be a major increase in adverse outcomes in newborns of mothers with MS. CONCLUSION: Although there are many unmet research needs, the reviewed data support the conclusion that in the majority of cases, women with MS can safely choose to become pregnant, give birth, and breastfeed children. Clinical management should be individualized to optimize both the mother's reproductive outcomes and MS course.
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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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".