National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: III. The 2014 Biomarker Working Group Report
Bibliographic record
Abstract
Biology-based markers to confirm or aid in the diagnosis or prognosis of chronic graft-versus-host disease (GVHD) after allogeneic hematopoietic cell transplantation or monitor its progression are critically needed to facilitate evaluation of new therapies. Biomarkers have been defined as any characteristic that is objectively measured and evaluated as an indicator of a normal biological or pathogenic process, or of a pharmacologic response to a therapeutic intervention. Applications of biomarkers in chronic GVHD clinical trials or patient management include the following: (1) diagnosis and assessment of chronic GVHD disease activity, including distinguishing irreversible damage from continued disease activity; (2) prognostic risk to develop chronic GVHD; and (3) prediction of response to therapy. Sample collection for chronic GVHD biomarkers studies should be well documented following established quality control guidelines for sample acquisition, processing, preservation, and testing, at intervals that are both calendar and event driven. The consistent therapeutic treatment of subjects and standardized documentation needed to support biomarker studies are most likely to be provided in prospective clinical trials. To date, no chronic GVHD biomarkers have been qualified for use in clinical applications. Since our previous chronic GVHD Biomarkers Working Group report in 2005, an increasing number of chronic GVHD candidate biomarkers are available for further investigation. This paper provides a 4-part framework for biomarker investigations: identification, verification, qualification, and application with terminology based on Food and Drug Administration and European Medicines Agency guidelines.
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 | 0.292 | 0.358 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.022 | 0.015 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.010 | 0.013 |
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".