Use of selective phenotyping and genotyping to identify rare blood donors in Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The distribution of rare and specific red cell phenotypes varies between races and ethnicities. Therefore, the most compatible red cell units for patients with haemoglobinopathies and other rare blood requirements are most likely to be found in donors from similar genetic backgrounds. Our blood service introduced a voluntary question asking donors to provide their racial background/ethnicity. Results triggered additional phenotyping and/or genotyping. MATERIALS AND METHODS: We analysed the results of additional testing performed between January 2021 and June 2022, and rare donors were added to the Rare Blood Donor database. We determined the incidence of various rare phenotypes and blood group alleles based on donor race/ethnicity. RESULTS: Over 95% of donors answered the voluntary question; 715 samples were tested, and 25 donors were added to the Rare Blood Donor database, including five k-, four U-, two Jk(a-b-) and two D- - phenotypes. CONCLUSION: Asking donors about their race/ethnicity was well received by donors, and the resulting selective testing enabled us to identify individuals with a higher likelihood of being rare blood donors, support patients with rare blood requirements and better understand the incidence of common and rare alleles and red blood cell phenotypes in the Canadian donor population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".