Upfront comprehensive donor search overcomes the impact of poor search prognosis, irrespective of racial background
Bibliographic record
Abstract
Abstract Introduction: Access to suitably matched donors remains a major barrier to allogeneic hematopoietic cell transplantation (HCT), particularly for non-White patients, who are underrepresented in global donor registries. Traditional donor search strategies vary in timing and resource intensity, often leading to delays or exclusion in racially diverse populations. At the Leukemia/BMT Program of British Columbia, which serves a multiethnic population under a publicly funded healthcare system, we implemented a strategy of simultaneous related and unrelated donor searches at diagnosis. This contrasts with the sequential, prognosis-guided approach supported by the recent BMT CTN 1702 trial. We hypothesized that early, comprehensive donor evaluation may mitigate the negative impact of poor donor search prognosis and lead to more equitable transplant access and outcomes across racial groups. Methods: We conducted a single-centre retrospective study of 542 consecutive adult patients with hematologic malignancies undergoing donor search between 2020–2024. Patients were categorized by self-identified race: White (n=401), Asian (n=107), and Other (n=34). Both transplanted and non-transplanted patients were analyzed. The primary endpoint was overall survival (OS); secondary endpoints included donor search prognosis (https://search-prognosis.b12x.org), donor availability, final donor type, time to HCT in acute leukemia patients in first complete remission (CR1), and 1-year HCT outcomes of non-relapse mortality (NRM), relapse incidence (RI), progression free survival (PFS). Statistical analyses included Kaplan-Meier survival curves, log-rank tests, and cumulative incidence for competing risks. Results: Despite differences in donor availability, transplantation rate were similar across groups (White: 70%, Asian: 74%, Other: 59%; p=0.9). Good donor search prognosis was significantly more common in White patients (57%) than in Asian (37%) and Other (10%) patients (p<0.0001). Availability of fully matched unrelated donors (MUD) also differed significantly (White: 76%, Asian: 59%, Other: 40%; p=0.0002). Non-White patients more frequently received alternative donors, especially haploidentical grafts (White: 9%, Asian: 24%, Other: 25%, p=0.001). Importantly, very few patients were unable to proceed to transplant due to donor unavailability (1 per group). Median time to HCT for acute leukemia in CR1 was comparable across races (Whites: 128 days, Asian: 127 days, Other: 135 days, p=0.4). One-year overall survival showed a statistically significant difference (White: 80%, Asian: 85%, Other: 50%; p=0.03), while relapse incidence (White: 10%, Asian: 14%, Other: 30%; p=0.1), non-relapse mortality (White: 12%, Asian: 4%, Other: 20%; p=0.3), and progression-free survival (White: 74%, Asian: 85%, Other: 60%; p=0.4) were not significantly different. Conclusion: In a public funded healthcare setting with centralized transplant coordination, a strategy of simultaneous donor search at diagnosis was associated with equitable transplant access and comparable transplant timing across racial groups, despite underlying differences in donor availability. While overall survival was lower in patients of other racial backgrounds, no statistically significant differences in relapse or non-relapse mortality suggest that the early search approach may mitigate traditional donor-related barriers. This model offers a promising alternative to sequential donor search algorithms and warrants further evaluation in diverse health systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".