National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: VI. Design of Clinical Trials Working Group Report
Bibliographic record
Abstract
The complexity of chronic graft-versus-host disease (GVHD) and the lack of established research methods have made it difficult to design, conduct, and analyze clinical trials involving subjects with this disease, even when promising treatment options are available. This consensus document was developed to offer an approach for overcoming these obstacles. Clinical trials in chronic GVHD should adhere to principles of good trial design and practice. Inclusion and exclusion criteria should allow as many subjects to participate as possible without compromising the interpretation of results. Pre-enrollment assessment of chronic GVHD characteristics should be standardized. The protocol should provide clear guidance about administration of study medication and other interventions. Methods of assessing response should be defined and validated in advance. Efficacy endpoints should be selected to reflect clinical benefit. Expert biostatistical support is needed to ensure the validity and reliability of trial results. The use of consistent standards in clinical trial designs to evaluate agents that have activity in pathogenic pathways could facilitate advances in the treatment of chronic GVHD.
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 | 0.498 | 0.510 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.021 | 0.014 |
| Research integrity | 0.028 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".