Haematopoietic SCT in severe autoimmune diseases: updated guidelines of the European Group for Blood and Marrow Transplantation
Bibliographic record
Abstract
In 1997, the first consensus guidelines for haematopoietic SCT (HSCT) in autoimmune diseases (ADs) were published, while an international coordinated clinical programme was launched. These guidelines provided broad principles for the field over the following decade and were accompanied by comprehensive data collection in the European Group for Blood and Marrow Transplantation (EBMT) AD Registry. Subsequently, retrospective analyses and prospective phase I/II studies generated evidence to support the feasibility, safety and efficacy of HSCT in several types of severe, treatment-resistant ADs, which became the basis for larger-scale phase II and III studies. In parallel, there has also been an era of immense progress in biological therapy in ADs. The aim of this document is to provide revised and updated guidelines for both the current application and future development of HSCT in ADs in relation to the benefits, risks and health economic considerations of other modern treatments. Patient safety considerations are central to guidance on patient selection and HSCT procedural aspects within appropriately experienced and Joint Accreditation Committee of International Society for Cellular Therapy and EBMT accredited centres. A need for prospective interventional and non-interventional studies, where feasible, along with systematic data reporting, in accordance with EBMT policies and procedures, is emphasized.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".