More precisely defining risk peri-HCT in pediatric ALL: pre- vs post-MRD measures, serial positivity, and risk modeling
Bibliographic record
Abstract
Detection of minimal residual disease (MRD) pre- and post-hematopoietic cell transplantation (HCT) for pediatric acute lymphoblastic leukemia (ALL) has been associated with relapse and poor survival. Published studies have had insufficient numbers to: (1) compare the prognostic value of pre-HCT and post-HCT MRD; (2) determine clinical factors post-HCT associated with better outcomes in MRD+ patients; and (3) use MRD and other clinical factors to develop and validate a prognostic model for relapse in pediatric patients with ALL who undergo allogeneic HCT. To address these issues, we assembled an international database including sibling (n = 191), unrelated (n = 259), mismatched (n = 56), and cord blood (n = 110) grafts given after myeloablative conditioning. Although high and very high MRD pre-HCT were significant predictors in univariate analysis, with bivariate analysis using MRD pre-HCT and post-HCT, MRD pre-HCT at any level was less predictive than even low-level MRD post-HCT. Patients with MRD pre-HCT must become MRD low/negative at 1 to 2 months and negative within 3 to 6 months after HCT for successful therapy. Factors associated with improved outcome of patients with detectable MRD post-HCT included acute graft-versus-host disease. We derived a risk score with an MRD cohort from Europe, North America, and Australia using negative predictive characteristics (late disease status, non-total body irradiation regimen, and MRD [high, very high]) defining good, intermediate, and poor risk groups with 2-year cumulative incidences of relapse of 21%, 38%, and 47%, respectively. We validated the score in a second, more contemporaneous cohort and noted 2-year cumulative incidences of relapse of 13%, 26%, and 47% (P < .001) for the defined risk groups.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".