236 Optimizing Haploidentical Donor Selection for Pediatric Hematopoietic Cell Transplant
Bibliographic record
Abstract
OBJECTIVES/GOALS: Patients who require a hematopoietic cell transplant (HCT) and dont have an HLA-matched related or unrelated donor may rely on a haploidentical donor. The optimal haploidentical donor and guidance for selection is limited. We aim to determine how donor characteristics affect outcomes following haploidentical-HCT for pediatric patients. METHODS/STUDY POPULATION: This is a retrospective cohort study evaluating the effect of donor age and relationship on post-HCT outcomes in children (0-18y) from 2008-2018. Multivariable logistic regression analysis will identify if donor age or donor relationship affect the development of graft-versus-host-disease (GVHD), while adjusting for other patient, donor, and transplant related variables. Two-year overall survival & event-free survival will be determined using Kaplan-Meier curves, stratified by donor age group and donor relationship, and compared by log-rank testing. Sub-analyses will be performed for myeloablative transplants and reduced intensity conditioning, as well as for malignant and non-malignant diseases. RESULTS/ANTICIPATED RESULTS: Our primary aim to is determine the effect of donor age and the effect of donor relationship to patient on the development of GVHD. We hypothesize that utilization of a younger donor will decrease the incidence of GVHD. Further, we hypothesize that utilizing a sibling haploidentical donor will result in less GVHD than a parental donor. Secondary aims include evaluating the effect of donor age and donor relationship on overall survival, event-free survival, non-relapse mortality, relapse, graft failure and time to engraftment. The results of this study will help us to develop criteria for optimal haploidentical donor selection. If donor selection is optimized, this could result in improved outcomes following haploidentical transplants. DISCUSSION/SIGNIFICANCE: Haploidentical donors are increasingly used as many patients, especially ethnic minorities, do not have an HLA-matched donor. This will be the largest study of haploidentical HCT in children. The data gathered will allow us to identify important donor characteristics to help guide physician decision-making when choosing a haploidentical donor.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".