Planning GvHD preemptive therapy: risk factors, biomarkers, and prognostic scores
Bibliographic record
Abstract
Prevention of acute and chronic graft-versus-host disease (aGvHD and cGvHD) is an important objective of allogeneic hematopoietic cell transplantation (HCT). While there is has been significant progress in preventative approaches in the peritransplant period to minimize development of GvHD, no preventative approach has completely eliminated development of either aGvHD or cGvHD. Recently, posttransplant immune biomarker profiling early post-HCT by the Mount Sinai Acute GvHD International Consortium group has resulted in a validated risk assignment algorithm and development of preemptive approaches to decrease aGvHD and mortality in high-risk patients. cGvHD risk assignment algorithms have been developed based on measurements at day 100 and may be used for future preemptive intervention trials to minimize cGvHD. This article discusses the current state of the art in aGvHD and cGvHD preemptive algorithms and therapeutic interventions and what is needed to move these into validated approaches.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".