Novel treatment concepts for graft-versus-host disease
Bibliographic record
Abstract
Acute and chronic graft-versus-host disease (GVHD) are potentially lethal complications after stem cell transplantation (SCT). Steroids are the appropriate first-line treatment for both. However, if patients do not adequately benefit from steroid therapy, mortality is high and standardized treatment algorithms are lacking. This is mainly because of limited data from prospective, randomized clinical trials. In addition, most of the available treatment options only induce clinical benefits in a limited proportion of patients. Thus, there is an urgent clinical need to develop more potent immunosuppressive treatment strategies for patients suffering from acute or chronic steroid-refractory GVHD while maintaining the graft versus tumor effect to avoid a potential rise in relapse-related mortality. The increasing knowledge about host- as well as donor-derived variables favoring GVHD development and the increasing armamentarium of immune-modulatory agents entering preclinical and clinical research will probably allow more effective treatment of GVHD in the future. This review describes novel developments in the treatment of steroid-refractory GVHD, with a special focus on the rationale behind promising pharmacologic compounds or up-coming cellular therapies.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".