National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: IIb. The 2020 Preemptive Therapy Working Group Report
Bibliographic record
Abstract
Chronic graft-versus-host disease (GVHD) commonly occurs after allogeneic hematopoietic cell transplantation (HCT) despite standard prophylactic immune suppression. Intensified universal prophylaxis approaches are effective but risk possible overtreatment and may interfere with the graft-versus-malignancy immune response. Here we summarize conceptual and practical considerations regarding preemptive therapy of chronic GVHD, namely interventions applied after HCT based on evidence that the risk of developing chronic GVHD is higher than previously appreciated. This risk may be anticipated by clinical factors or risk assignment biomarkers or may be indicated by early signs and symptoms of chronic GVHD that do not fully meet National Institutes of Health diagnostic criteria. However, truly preemptive, individualized, and targeted chronic GVHD therapies currently do not exist. In this report, we (1) review current knowledge regarding clinical risk factors for chronic GVHD, (2) review what is known about chronic GVHD risk assignment biomarkers, (3) examine how chronic GVHD pathogenesis intersects with available targeted therapeutic agents, and (4) summarize considerations for preemptive therapy for chronic GVHD, emphasizing trial development, including trial design and statistical considerations. We conclude that robust risk assignment models that accurately predict chronic GVHD after HCT and early-phase preemptive therapy trials represent the most urgent priorities for advancing this novel area of research.
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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 | 0.326 | 0.388 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.016 | 0.013 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".