Disease course after pregnancy in women with progressive multiple sclerosis symptoms
Bibliographic record
Abstract
BACKGROUND: The impact of pregnancy on disease outcomes has not been characterised in women with progressive multiple sclerosis (MS) phenotypes. This study aimed to describe the clinical characteristics and disease course of women who experienced a pregnancy after a diagnosis of primary progressive MS (PPMS) or secondary progressive MS (SPMS). METHODS: This multicentre observational cohort study utilised data from the international MSBase Registry extracted on 2 June 2024. Expanded Disability Status Scale (EDSS) scores of women with progressive MS were assessed up to 10 years postpartum and compared to those of propensity score-matched women with progressive MS without a pregnancy history. RESULTS: In total, 138 women with 164 pregnancies were included in the study, comprising 75 women with PPMS and 63 with SPMS. Of these, 24 women with PPMS and 47 with SPMS had longitudinal peri-pregnancy EDSS assessments and were included in the analysis of disability scores. A history of pregnancy was not associated with a significant difference in long-term disability trajectories in women with either PPMS (estimate = -0.02; 95% confidence interval (CI) = -0.07 to 0.04) or SPMS (estimate = 0.00; 95% CI = -0.02 to 0.03). CONCLUSION: A history of pregnancy is not associated with a significant difference in long-term disability in women with progressive MS symptoms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".