Innovative cellular therapies for autoimmune diseases: expert-based position statement and clinical practice recommendations from the EBMT practice harmonization and guidelines committee
Bibliographic record
Abstract
Autoimmune diseases (ADs) are characterized by loss of immune tolerance, high chronicity, with substantial morbidity and mortality, despite conventional immunosuppression (IS) or targeted disease modifying therapies (DMTs), which usually require repeated administration. Recently, novel cellular therapies (CT), including mesenchymal stromal cells (MSC), Chimeric Antigen Receptors T cells (CART) and regulatory T cells (Tregs), have been successfully adopted in ADs. An international expert panel of the European Society for Blood and Marrow Transplantation and the International Society for the Cell and Gene Therapy, reviewed all available evidence, based on the current literature and expert practices, on use of MSC, CART and Tregs, in AD patients with rheumatological, neurological, and gastroenterological indications. Expert-based consensus and recommendations for best practice and quality of patient care were developed to support clinicians, scientists, and their multidisciplinary teams, as well as patients and care providers and will be regularly updated.
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 | 0.075 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".