Biomarkers in newly diagnosed pediatric-extensive chronic graft-versus-host disease: a report from the Children's Oncology Group
Bibliographic record
Abstract
Numerous chronic graft-versus-host disease (cGVHD) biomarkers have been identified in limited, single-institution studies without validation. We hypothesized that plasma-derived biomarkers could diagnose, classify, and evaluate response in children with cGVHD. We performed a concomitant analysis of a number of known and predicted peripheral blood cGVHD biomarkers from a Children's Oncology Group (COG) phase 3 cGVHD therapeutic trial. A total of 52 newly diagnosed patients with extensive cGVHD were compared for time of onset after blood and marrow transplantation (BMT) (early, 3-8 months; late, > or = 9 months) with 28 time-matched controls with no cGVHD (early, 6 months after BMT; late, 12 months after BMT). Soluble B-cell activation factor (sBAFF), anti-dsDNA antibody, soluble IL-2 receptor alpha (sIL-2Ralpha), and soluble CD13 (sCD13) were elevated in patients with early-onset cGVHD compared with controls. sBAFF and anti-dsDNA were elevated in patients with late-onset cGVHD. Some of the biomarkers correlated with specific organ involvement and with therapeutic response. These 4 biomarkers had high specificity with higher sensitivity in combination. Changes in biomarker concentrations with immune reconstitution after transplantation significantly affected interpretation of results. The identified biomarkers have the potential for improved classification, early response evaluation, and direction of cGVHD treatment, but require validation in larger studies. This study is registered at www.cancer.gov/clinicaltrials as no. COG-ASCT0031.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".