Evaluation of pregnancy outcomes in patients with multiple sclerosis after fingolimod exposure
Bibliographic record
Abstract
BACKGROUND AND METHODS: Limited data are available on the safety of fingolimod in pregnant women. We estimated the risk of adverse pregnancy outcomes in women with multiple sclerosis (MS) exposed to fingolimod either shortly before or during pregnancy in prospectively collected cases from clinical trials, observational studies, surveillance programs, and spontaneous reports. RESULTS: The prevalence of major malformations among live births does not appear to be significantly higher than those in the general population and the unexposed MS population. Similarly, the prevalence of cardiac malformations observed in this analysis was not significantly different from that of the general population. Proportions of miscarriage were in line with those of the general and unexposed MS population and no specific pattern of birth defects was identified. CONCLUSIONS: These data can help inform healthcare professionals and women with MS exposed to fingolimod during conception.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".