Modeling unrelated blood stem cell donor recruitment using simulated registrant cohorts: Assessment of <scp>human leukocyte antigen</scp> matching across ethnicity groups
Bibliographic record
Abstract
BACKGROUND: Human leukocyte antigen (HLA)-matched unrelated donors are not available for some patients considered for allogeneic hematopoietic cell transplantation, particularly among certain ethnic groups. Simulated recruitment modeling can inform efforts to find new matches for more patients. METHODS: Simulated recruits were generated by assigning a pair of donor HLA haplotypes from historical data files and matched against HLA data of patient searches in the Canadian Blood Services Stem Cell Registry. Recruitment cohorts reflected the proportion of five specific ethnic groups in the 2016 Canadian census data. RESULTS: Novel 8/8 HLA matches between simulated recruits and patients increased linearly with larger recruitment cohorts. The proportion of novel 8/8 HLA matches from Caucasian, Hispanic, and Native American/First Nations recruits was equal to or greater than their relative proportion in the recruited cohort (match to: recruit ratio (MRR) ≥ 1). In contrast, African American and Asian & Pacific Islander recruits represented a smaller proportion of novel matches relative to their percentage of the recruited cohort (MRR <1). The proportion of novel 7/8 HLA-matches from each ethnic group was approximately the same as their proportion in the recruited cohort (MRR ~ 1) and high rates of 7/8 HLA-matching already exist within the Canadian Blood Services registry for all ethnic groups. CONCLUSION: Continued large recruitment cohorts are needed to add new 8/8 HLA matches to registry inventories. Likelihoods of novel HLA matches varied across ethnic groups, reflecting varied HLA haplotype frequencies across groups. Simulated cohort modeling can inform recruitment strategies that will generate new donor options for patients.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".