Pregnancy outcomes in patients with MS following exposure to ofatumumab: Updated results (Novartis safety database)
Bibliographic record
Abstract
The Novartis Safety Database collected cases from clinical trials and through a post-marketing phar- macovigilance non-interventional PRegnancy outcomes Intensive Monitoring (PRIM) study, where data from spontaneously reported pregnancies were collected using a set of targeted structured checklists. Pregnancy outcomes in women with MS exposed to ofatumumab during pregnancy or 6 months prior to last menstrual period (LMP) were analyzed and will be reported. Pregnancy and infant outcomes including congenital anomalies, infections, vaccinations, and developmental delays were collected from the reporting of pregnancy up to 1 year of infant age. At prior cutoff date of March-25-2022, there were 61 exposed pregnancies with 30 known outcomes and 17 live births after maternal exposure to ofatumumab during pregnancy or 6 months prior to LMP. No congenital anomalies, reports of B-cell depletion, immunoglobulin/hematological abnormalities, or serious infections were reported. Updated pregnancy outcomes with a cutoff date of Sep-25-2022 from the Database will be presented at the congress. Reporting the latest data on pregnancy outcomes after exposure to ofatumumab will provide informa- tion to healthcare professionals who treat people with MS of childbearing potential. In addition to the Novartis sponsored PRIM initiative, a prospective observational exposure registry on maternal and infant outcomes in patients exposed to ofatumumab is also underway.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".