Genetics by genetic algorithm: Defining an ideal platelet donor population to support patients with <scp>HLA</scp> ‐mediated alloimmune refractoriness
Bibliographic record
Abstract
BACKGROUND: Immune factors underlie approximately 20% of patients exhibiting platelet transfusion refractoriness (PTR). PTR can be mitigated by supplying HLA-A and B compatible platelets to these patients. Since 2019, Canadian Blood Services has managed a program to provide compatible platelets to alloimmunized patients. This study evaluates donor pool size and ethnic diversity options to serve Canada's population. STUDY DESIGN AND METHODS: We adapted simulation-based stem cell matching methods by generating simulated patients and matching them to existing and simulated donors using a Python algorithm. Recruitment scenarios included census-aligned, single-group, and meta-heuristic-optimized distributions to enhance match rates and equity. RESULTS: More typed donors increase match rates, but rates vary significantly by ethnicity. Non-Black patients achieved 95% coverage with 13,000-18,000 new donors, whereas Black patients required ~55,000 new donors. Single-ethnicity recruitment improved the targeted group's rate but reduced overall match rates and disadvantaged other populations. A Pareto-optimal donor mix-augmenting Black, Hispanic, Asian Pacific Islander, and Native American/First Nations donors while modestly reducing White typing-preserved or improved rates but yielded only modest gains for Black patients. A balanced strategy further prioritizing Black and Hispanic donors with slight reductions in White, Asian, and Native American/First Nations typing achieved improvements for the Black population with minimal losses for others. DISCUSSION: While Pareto-optimal allocation enhances overall efficiency, targeted trade-offs, focusing on underrepresented groups, are essential to correct persistent disparities and achieve equitable HLA-matched platelet availability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".