Recruiting blood donors to the Canadian Blood Services Stem Cell Registry: A feasibility assessment
Bibliographic record
Abstract
BACKGROUND: Allogeneic hematopoietic cell transplantation remains limited when stem cell registrants cannot be contacted, are not medically fit, are unavailable, or unwilling to proceed. In a recent report, registrants who were prior blood donors were more likely to be available for donation. In this study, we analyzed extent to which recruiting blood donors to the Canadian Blood Services Stem Cell Registry (CBS SCR) can meet targets for ethnic diversity, age, and proximity to collection facilities. METHODS AND RESULTS: We analyzed 124,496 active blood donors on July 1, 2023 regarding the criteria for recruitment to the CBS SCR. A total of 40,518 (32%) were younger than 36 years of age and 49% were first-time donors (potential new recruits year over year). The ethnicity of blood donors younger than 36 years aligns more closely with the 2021 Canadian census compared to stem cell donors who were also previous blood donors, and to the current total inventory of all registrants on the CBS SCR. Of the blood donors, certain ethnic groups, including Black, Chinese, and First Nations/Indigenous, remain underrepresented. A greater proportion of active whole blood donors live within 400 km of a stem cell collection center (91%) compared to stem cell donors who donated during the past 10 years (80%). CONCLUSIONS: Recruitment of blood donors offers an opportunity to improve the ethnic diversity of the CBS SCR and increase proximity of registrants to stem cell collection centers. The potential improved availability of registrants when matched to patients requires confirmation.
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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 | 0.090 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".