Bibliographic record
Abstract
INTRODUCTION: Graft-versus-host disease (GVHD) leads to significant morbidity and mortality after allogeneic stem cell transplantation. While corticosteroids alone are adequate in some cases, they are often insufficient, leading to poor quality of life associated with the symptoms of disease, or mortality from infection and GVHD. Moreover, corticosteroids have significant side effects and often do not lead to durable responses. New therapies are needed to improve the development and progression of acute and chronic GVHD. AREAS COVERED: We discuss the spectrum of emerging drugs for GVHD prevention and therapy. Cellular therapies will be briefly discussed. The available pre-clinical and clinical data regarding monoclonal antibodies, interleukin-2, alpha-1 antitrypsin, histone deacetylase inhibitors, tyrosine kinase inhibitors, and proteasome inhibitors will be reviewed. EXPERT OPINION: Although therapies emerging for GVHD remain promising, most of these drugs are still in early phase clinical trials and require randomized comparisons before formal conclusions can be drawn. It is likely that in the near future some of these agents will show improvements in response when compared with corticosteroids alone. Although it is difficult to predict which of these agents will be most promising, alpha-1 antitrypsin, ruxolitinib and interleukin-2 have demonstrated encouraging results.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".