Outcomes of pregnancy during interferon beta‐1a therapy in Japanese patients with multiple sclerosis: Interim results of a postmarketing surveillance study
Bibliographic record
Abstract
Abstract Objectives To evaluate pregnancy outcomes in Japanese patients with multiple sclerosis (MS) enrolled in a postmarketing surveillance study of intramuscular interferon beta‐1a (IM IFN beta‐1a). Methods Safety data were collected from Japanese patients receiving 30 μg weekly IM IFN beta‐1a. Pregnancy outcomes and annualized relapse rates (ARR) were analyzed retrospectively in patients who registered into the postmarketing surveillance study. Results A total of 1110 of 1638 patients registered in the postmarketing surveillance study were women. A total of 21 pregnancies (20 patients) resulted in 17 live births, 2 induced abortions, one spontaneous abortion and one unknown outcome. Weights and lengths of the 17 newborns were similar to newborns in the general Japanese population. No complications or malformations were reported. Of these 20 patients, nine experienced relapses in the year after childbirth, two experienced relapses in the year before pregnancy, and one experienced relapses before and during pregnancy. The mean (standard deviation) ARR was 0.94 (2.18) in the year before pregnancy, 0.25 (1.00), 0 (0) and 0 (0) in the three trimesters of pregnancy, and 1.05 (1.81), 0.84 (1.68), 0.63 (2.01) and 0.21 (0.92) in the first four quarters postpartum. Of the five patients who relapsed during the first quarter postpartum, only one had resumed IFN beta‐1a treatment for 35 days. Conclusions Although sample size limits our ability to draw definitive conclusions, we did not find evidence that IFN beta‐1a has adverse effects on pregnancy outcomes in Japanese patients with MS. Early IFN beta‐1a resumption might reduce the risk of relapse within the first quarter postpartum.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".