Bibliographic record
Abstract
Chronic graft-versus-host disease (cGVHD ) is a leading cause of allogeneic hematopoietic stem-cell transplantation-related mortality and morbidity. It is an immune-mediated disorder that can target almost any organ in the body, often with devastating consequences. The immune-suppressive medications currently used to treat it are equally toxic and are often not very effective. At this time, our understanding of its pathophysiology is limited. The discovery of potential biomarkers offers new possibilities in the clinical management of cGVHD. They could potentially be used for diagnosing cGVHD, for predicting or evaluating response to therapy and for unique insights into the pathophysiology underlying the clinical manifestations of cGVHD. Understanding the biological origins of these biomarkers can help us construct a more comprehensive and clinically relevant model for the pathogenesis of this disease. In this article, we review existing evidence for candidate biomarkers that have been identified in the framework of how they may contribute to the pathophysiology of cGVHD. Issues regarding the discovery and application of biomarkers are discussed.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".