Refractory myasthenia gravis treated with autologous hematopoietic stem cell transplantation
Bibliographic record
Abstract
OBJECTIVES: Patients with refractory myasthenia gravis (MG) have few treatment options. Autologous hematopoietic stem cell transplantation (HSCT) has been used to treat immune diseases; however, its use in the treatment of MG is not broadly considered. Our objective is to report on the efficacy and safety of HSCT in refractory MG. METHODS: Twenty-one patients who underwent HSCT for MG were retrospectively reviewed. All patients had severe MG refractory to multiple therapies. Stem cells were mobilized with cyclophosphamide and granulocyte colony-stimulating factor. The grafts were depleted of immune cells by selecting CD34+ cells. HSCT conditioning consisted of high-dose cytoreductive therapy and anti-thymocyte globulin. The primary efficacy outcome was achieving clinically stable remission or minimal manifestations without treatment and remaining as such until most recent follow-up. RESULTS: The median time from MG diagnosis to HSCT was 4.0 years. The primary outcome was reached in 16 of 18 evaluable patients (89%) at a median of 1.7 years and maintained with a median follow-up of 6.7 years (range 1.0-21.9 years). Three patients were not evaluable for the primary outcome: one due to confounding illness and two died within 12 months of transplant. The transplant-related mortality at 100 days was 9.5%. Two late deaths occurred, with uncertain relation to the HSCT. INTERPRETATION: After HSCT for refractory MG, most patients achieved sustained disease remission. However, HSCT-related mortality in medically complex MG patients may be high. Prospective studies investigating the efficacy and safety of HSCT in the treatment of refractory MG are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".