<scp>Anti‐CD20</scp> monoclonal antibody therapy in postpartum women with neurological conditions
Bibliographic record
Abstract
OBJECTIVE: Postpartum, patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) have increased risk for disease activity. Anti-CD20 IgG1 monoclonal antibodies (mAb) are increasingly used as disease-modifying therapies (DMTs). Patients may wish to both breastfeed and resume DMT postpartum. This study aimed to determine the transfer of anti-CD20 IgG1 mAbs, ocrelizumab, and rituximab (OCR/RTX), into mature breastmilk and describe maternal and infant outcomes. METHODS: Fifty-seven cis-women receiving OCR/RTX after 59 pregnancies and their infants were enrolled and followed up to 12M postpartum or 90 days post-infusion. Breastmilk was collected pre-infusion and serially up to 90 days and assayed for mAb concentration. Medical records and patients' questionnaire responses were obtained to assess neurologic, breastfeeding, and infant development outcomes. RESULTS: The median average concentration of mAb in breastmilk was low (OCR: 0.08 μg/mL, range 0.05-0.4; RTX: 0.03 μg/mL, range 0.005-0.3). Concentration peaked 1-7 days post-infusion in most (77%) and was nearly undetectable after 90 days. Median average relative infant dose was <1% (OCR: 0.1%, range 0.07-0.7; RTX: 0.04%, range 0.005-0.3). Forty-three participants continued to breastfeed post-infusion. At 8-12 months, the proportion of infants' growth between the 3rd and 97th World Health Organization percentiles did not differ for breastfed (36/40) and non-breastfed (14/16, p > 0.05) infants; neither did the proportion with normal development (breastfed: 37/41, non-breastfed: 11/13; p > 0.05). After postpartum infusion, two mothers experienced a clinical relapse. INTERPRETATION: These confirm minimal transfer of mAb into breastmilk. Anti-CD20 mAb therapy stabilizes MS activity before conception to the postpartum period, and postpartum treatments appears to be safe and well-tolerated for both mother and infant.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".