Toward Biomarkers for Chronic Graft-versus-Host Disease: National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: III. Biomarker Working Group Report
Bibliographic record
Abstract
Biology-based markers that can be used to confirm the diagnosis of chronic graft-versus-host disease (GVHD) or monitor progression of the disease could help in the evaluation of new therapies. Biomarkers have been defined as any characteristic that is objectively measured and evaluated as an indicator of a normal biologic or pathogenic process, a pharmacologic response to a therapeutic intervention, or a surrogate end point intended to substitute for a clinical end point. The following applications of biomarkers could be useful in chronic GVHD clinical trials or management: (1) predicting response to therapy; (2) measuring disease activity and distinguishing irreversible damage from continued disease activity; (3) predicting the risk of developing chronic GVHD; (4) diagnosing chronic GVHD: (5) predicting the prognosis of chronic GVHD; (6) evaluating the balance between GVHD and graft-versus-leukemia effects (graft-versus-leukemia or GVT); and (7) serving as a surrogate end point for therapeutic response. Such biomarkers can be identified by either hypothesis-driven testing or by high-throughput discovery-based methods. To date, no validated biomarkers have been established for chronic GVHD, although several candidate biomarkers have been identified from limited hypothesis-driven studies. Both approaches have merit and should be pursued. The consistent treatment and standardized documentation needed to support biomarker studies are most likely to be satisfied in prospective clinical trials.
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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 | 0.383 | 0.314 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.019 | 0.014 |
| Research integrity | 0.024 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".