P.035 Autologous hematopoietic stem cell transplant for the treatment of refractory myasthenia gravis with anti-muscle specific kinase antibodies
Bibliographic record
Abstract
Background: Several case series describe patients with refractory acetylcholine receptor antibody-positive (AChR) myasthenia gravis (MG) treated with hematopoietic stem cell transplant (HSCT). In this report, we describe four patients with anti-muscle-specific kinase (MuSK)MG treated with HSCT. Methods: We reviewed the records of all patients undergoing HSCT for MG in the Alberta Blood and Bone Marrow Transplant Program and identified 4 patients with anti-MuSK MG. Results: All 4 patients had severe disease (Myasthenia Gravis Foundation of America score IVb-V) and were refractory to multiple treatments, including rituximab. 3 patients improved with no clinical manifestations or mild symptoms and remained as such for 2, 3.5, and 5.5 years. In these 3 patients, adverse events ranged from treatable infections and transient dyspnea to persistent fatigue and premature menopause. The average worst Myasthenia Gravis Activities of Daily Living (MG-ADL) scores improved from 14.7 before to 0.3 after HSCT while their mean worst Myasthenia Gravis Quality of Life Questionnaire (MG-QoL15) scores improved from 26.7 to 0. The fourth patient developed pneumonia and passed away from respiratory failure 8 weeks post-transplant. Conclusions: In patients with severe refractory anti-MuSK MG, it may be reasonable to consider HSCT but with an appreciation of the associated risks.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".