General information for patients and carers considering haematopoietic stem cell transplantation (HSCT) for severe autoimmune diseases (ADs): A position statement from the EBMT Autoimmune Diseases Working Party (ADWP), the EBMT Nurses Group, the EBMT Patient, Family and Donor Committee and the Joint Accreditation Committee of ISCT and EBMT (JACIE)
Bibliographic record
Abstract
Over the last 20 years, haematopoietic stem cell transplantation (HSCT) has been used to treat patients with severe autoimmune and inflammatory diseases whose response to standard treatment options has been limited, resulting in a poor long-term prognosis in terms of survival or disability. The vast majority of patients have received autologous HSCT where an increasing evidence-base supports its use in a wide range of autoimmune diseases, particularly relapsing remitting MS, systemic sclerosis and Crohn's disease. Compared with standard treatments for autoimmune diseases, HSCT is associated with greater short-term risks, including a risk of treatment-related mortality and long-term complications. There is a need for a careful appraisal of potential benefits and risks by disease and transplant specialists working closely together with patients and carers to determine individual suitability for HSCT. HSCT should be conducted in accredited transplant centres with robust arrangements for long-term follow-up with both disease and transplant specialists. The aim of this open-access position statement is to provide plainly worded guidance for patients and non-specialist clinicians considering HSCT for an autoimmune disease, especially when treatment abroad is being considered. Recent technical publications in the field have been referenced to support the statement and provide more detail for clinicians advising patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.125 | 0.079 |
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".