A diagnostic classifier for pediatric chronic graft-versus-host disease: results of the ABLE/PBMTC 1202 study
Bibliographic record
Abstract
The National Institutes of Health Consensus criteria for chronic graft-versus-host disease (cGVHD) diagnosis can be challenging to apply in children, making pediatric cGVHD diagnosis difficult. We aimed to identify diagnostic pediatric cGVHD biomarkers that would complement the current clinical criteria and help differentiate cGVHD from non-cGVHD. The Applied Biomarkers of Late Effects of Childhood Cancer (ABLE) study, open at 27 transplant centers, prospectively evaluated 302 pediatric patients after hematopoietic cell transplant (234 evaluable). Forty-four patients developed cGVHD. Mixed and fixed effect regression analyses were performed on diagnostic cGVHD onset blood samples for cellular and plasma biomarkers, with individual markers declared relevant if they met 3 criteria: an effect ratio ≥1.3 or ≤0.75; an area under the curve (AUC) of ≥0.60; and a P value <5.814 × 10-4 (Bonferroni correction) (mixed effect) or <.05 (fixed effect). To address the complexity of cGVHD diagnosis in children, we built a machine learning-based classifier that combined multiple cellular and plasma biomarkers with clinical factors. Decreases in regulatory natural killer cells, naïve CD4 T helper cells, and naïve regulatory T cells, and elevated levels of CXCL9, CXCL10, CXCL11, ST2, ICAM-1, and soluble CD13 (sCD13) characterize the onset of cGVHD. Evaluation of the time dependence revealed that sCD13, ST2, and ICAM-1 levels varied with the timing of cGVHD onset. The cGVHD diagnostic classifier achieved an AUC of 0.89, with a positive predictive value of 82% and a negative predictive value of 80% for diagnosing cGVHD. Our polyomic approach to building a diagnostic classifier could help improve the diagnosis of cGVHD in children but requires validation in future prospective studies. This trial was registered at www.clinicaltrials.gov as #NCT02067832.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".