The Stem Cell Club: a model for unrelated stem cell donor recruitment
Bibliographic record
Abstract
BACKGROUND: Patients with blood, immune, or metabolic diseases may require a stem cell transplant as part of their treatment. However, 70% of patients do not have a suitable human leukocyte antigen match in their family, and need an unrelated donor. Individuals can register as potential donors at stem cell drives, where they provide consent and a tissue sample for human leukocyte antigen typing. The ideal donors are young, male, and from a diversity of ethnic backgrounds. However, in Canada, non-Caucasian males ages 17 to 35 years represent only 8.8% of listed donors. STUDY DESIGN AND METHODS: The Stem Cell Club is a non-profit organization founded in 2011 in Canada that aims to augment recruitment of the most needed donors. The initiative published a recruitment toolkit online (www.stemcellclub.ca). Currently, there are 12 chapters at universities across Canada. RESULTS: To date, the Stem Cell Club has recruited 6585 potential registrants, representing 1.63% of donors on Canada's donor-database. Of the recruited registrants, 58.3% were male; 60.3% of males self-reported as non-Caucasian, and 78.5% were ages 17 to 25 years. From 2015 to 2016, the initiative recruited 13.7% of all ethnically diverse males ages 17 to 35 years listed in Canada's donor database. Data from this initiative demonstrate sustainability and performance on key indicators of stem cell drive quality. CONCLUSION: The Stem Cell Club has developed a capacity to recruit 2600 donors annually, with the majority being males with a high degree of ethnic diversity. The initiative enhances the quality of Canada's unrelated donor-database, improving the chances that patients in need of an unrelated donor will find a match for transplant. The Stem Cell Club is a model relevant to recruitment organizations around the world.
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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 | 0.035 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.017 |
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".