Predictors and dynamics of postpartum relapses in women with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Several studies have shown that pregnancy reduces multiple sclerosis (MS) relapses, which increase in the early postpartum period. Postpartum relapse risk has been predicted by pre-pregnancy disease activity in some studies. OBJECTIVE: To re-examine effect of pregnancy on relapses using the large international MSBase Registry, examining predictors of early postpartum relapse. METHODS: An observational case-control study was performed including pregnancies post-MS onset. Annualised relapse rate (ARR) and median Expanded Disability Status Scale (EDSS) scores were compared for the 24 months pre-conception, pregnancy and 24 months postpartum periods. Clustered logistic regression was used to investigate predictors of early postpartum relapses. RESULTS: The study included 893 pregnancies in 674 females with MS. ARR (standard error) pre-pregnancy was 0.32 (0.02), which fell to 0.13 (0.03) in the third trimester and rose to 0.61 (0.06) in the first three months postpartum. Median EDSS remained unchanged. Pre-conception ARR and disease-modifying treatment (DMT) predicted early postpartum relapse in a multivariable model. CONCLUSION: Results confirm a favourable effect on relapses as pregnancy proceeds, and an early postpartum peak. Pre-conception DMT exposure and low ARR were independently protective against postpartum relapse. This novel finding could provide clinicians with a strategy to minimise postpartum relapse risk in women with MS planning pregnancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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".