National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: IIa. The 2020 Clinical Implementation and Early Diagnosis Working Group Report
Bibliographic record
Abstract
Recognition of the earliest signs and symptoms of chronic graft-versus-host disease (GVHD) that lead to severe manifestations remains a challenge. The standardization provided by the National Institutes of Health (NIH) 2005 and 2014 consensus projects has helped improve diagnostic accuracy and severity scoring for clinical trials, but utilization of these tools in routine clinical practice is variable. Additionally, when patients meet the NIH diagnostic criteria, many already have significant morbidity and possibly irreversible organ damage. The goals of this early diagnosis project are 2-fold. First, we provide consensus recommendations regarding implementation of the current NIH diagnostic guidelines into routine transplant care, outside of clinical trials, aiming to enhance early clinical recognition of chronic GVHD. Second, we propose directions for future research efforts to enable discovery of new, early laboratory as well as clinical indicators of chronic GVHD, both globally and for highly morbid organ-specific manifestations. Identification of early features of chronic GVHD that have high positive predictive value for progression to more severe manifestations of the disease could potentially allow for future pre-emptive clinical trials.
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 | 0.396 | 0.393 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.019 | 0.016 |
| Research integrity | 0.023 | 0.030 |
| Insufficient payload (model declined to judge) | 0.007 | 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".