The MAGIC composite response: a novel end point integrating clinical and biomarker parameters for acute GVHD
Bibliographic record
Abstract
ABSTRACT: Changes in the clinical symptoms of acute graft-versus-host disease (GVHD) are currently used to assess treatment responses. The Mount Sinai Acute GVHD International Consortium (MAGIC) consortium has recently revealed that the integration of serum biomarkers with clinical symptoms at the onset of treatment in a MAGIC composite score (MCS) more accurately predicts treatment response and 6-month nonrelapse mortality (NRM) than clinical symptoms alone. In this study, we evaluated whether the integration of serum biomarkers and clinical symptoms on day 28 (D28) would also better predict NRM than clinical response only (CRO). We analyzed data from 1135 patients receiving systemic treatment for acute GVHD and created a fourth MCS category for patients with complete resolution of symptoms and low-risk clinical biomarkers on D28. Using a classification and regression tree model with 6-month NRM as the end point, we identified status of MCS 0 or MCS 1 at D28 as responses, which we termed the MAGIC composite response (MCR). In the validation cohort (n = 309), MCR more accurately predicted 6-month NRM than CRO (area under the curve: 0.77 vs 0.69; P = .014) and demonstrated higher negative and positive predictive values. MCR correctly reclassified both clinical nonresponders and responders: 28 of 213 clinical responders (13%) became nonresponders with fivefold higher NRM (34.3% vs 6.8%, P < .001) and a larger group (29/96, 30%) of clinical nonresponders became responders with sixfold lower NRM (7.6% vs 50.7%, P < .001). These findings support the use of MCR as a superior surrogate end point for long-term GVHD control and survival in future clinical trials.
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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 | 0.000 |
| 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".