Development and validation of a pediatric disease risk index for allogeneic hematopoietic cell transplantation.
Bibliographic record
Abstract
7503 Background: Characteristics such as disease, disease status and cytogenetic abnormalities impact relapse and survival after transplantation for acute myeloid (AML) and acute lymphoblastic (ALL) leukemia. In adults, these attributes were used to derive the disease risk index for survival. Thus, the current analysis sought to develop and validate a pediatric disease risk index (p-DRI). Methods: Eligible were patients aged <18 years with AML (n=1135) and ALL (n=1228) transplanted between 2008 and 2017 in the United States. Separate analyses were performed for AML and ALL. Patients were randomly assigned (1:1) to a training and validation cohort. Cox proportional hazards model with stepwise selection was used to select significant variables (2-sided p<0.05). The primary outcome was leukemia-free survival (LFS; relapse or death were events). Based on the magnitude of log(HR), a weighted score was assigned to each characteristic that met the level of significance and risk groups were created. Results: Four risk groups were identified for AML and three risk groups for ALL (Table). The 5-year probabilities of LFS for AML were 81% (68-91), 56% (51-61), 44% (39-49) and 21% (15-28) for good, intermediate, high and very high-risk groups, respectively. The 5-year probabilities of LFS for ALL were 68% (63-72), 50% (45-54) and 15% (3-34) for good, intermediate, high risk groups, respectively. Conclusions: This validated p-DRI successfully stratified children with AML and ALL for prognostication undergoing allogeneic transplantation. [Table: see text]
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".