P.011 Measuring cord blood b cells in neonates with possible exposure in utero to Anti-B cell therapies
Bibliographic record
Abstract
Background: The increasing use of anti-B cell therapies in managing multiple sclerosis(MS) around the time of conception has raised important considerations for neonates exposed in utero. International recommendations suggest assessing neonatal B cell count in potentially exposed neonates. Practical implementation of cord blood collection at birth requires coordinated care across specialties, including paediatric haematology, neurology and obstetrics. Methods: This workshop, scheduled for 01/30/2025, will address clinical and logistical challenges of neonatal B cell assessments following in utero exposure to anti-B cell therapies. Presentations by MS pregnancy specialists from Toronto,Ontario, will be complemented by collaborative problem-solving among participants, including a paediatric haematologist, MS neurologists, obstetricians, paediatricians, and a quality specialist. A patient with lived experience will contribute to discussions. Results: The workshop will develop a care pathway for cord blood B cell testing, optimizing vaccine scheduling at London Health Sciences Center (LHSC) in London Ontario. Outcomes will include enhanced multidisciplinary collaboration, participant feedback, development of a practical clinical care plan for B cell collection and interpretation and measures of the pathway’s impact on patient satisfaction and clinical decisions. Conclusions: This initiative will improve care for mothers and neonates exposed to anti-B cell therapies, addressing critical gaps in clinical practice through collaboration and a standardized approach.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".