Autologous hematopoietic stem cell transplant for the treatment of refractory myasthenia gravis with <scp>anti‐muscle</scp> specific kinase antibodies
Bibliographic record
Abstract
INTRODUCTION/AIMS: Up to 25% of patients with myasthenia gravis (MG) have refractory disease despite trials of multiple immunosuppressants. Several case series describe acetylcholine receptor antibody-positive (AChR) MG patients treated with autologous hematopoietic stem cell transplant (HSCT). In this report, we describe three patients with anti-muscle-specific kinase (MuSK) MG treated with HSCT. METHODS: We included all patients who had undergone HSCT with anti-MuSK myasthenia gravis identified through the records of the Alberta Blood and Marrow Transplant Program. We collected demographic and clinical data including validated MG scales as well as questionnaire data. RESULTS: All 3 patients had severe disease (Myasthenia Gravis Foundation of America score IVb-V) and were refractory to multiple treatments, including rituximab. All patients improved with no clinical manifestations or mild symptoms and remained as such for 2, 3.5, and 5.5 y. 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. The mean worst Myasthenia Gravis Quality of Life Questionnaire (MG-QoL15) scores improved from 26.7 to 0. All patients reported they would undergo transplant again for their MG. DISCUSSION: We describe three patients with anti-MuSK MG treated with HSCT, all of whom became symptom free from MG with a tolerable side effect profile. In patients with severe refractory anti-MuSK MG, it may be reasonable to consider HSCT.
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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 | 0.001 | 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".