Targeted recruitment of optimal donors for unrelated hematopoietic cell transplantation
Bibliographic record
Abstract
OBJECTIVE/BACKGROUND: Patients in need of hematopoietic stem cell transplantation often cannot find a suitable HLA-matched donor in their families and rely on unrelated donors. Individuals can register with their country's donor registry either online or at a stem cell drive by providing consent and a tissue sample for typing. METHODS: Stem Cell Club is a donor recruitment organization in Canada that recruits Canadians as stem cell donors. This article outlines the Stem Cell Club's protocol for donor recruitment at stem cell drives including five core components: prescreening, informed consent, registration, tissue sample collection, and reconciliation. RESULTS: At stem cell drives, recruiters approach individuals from the most-needed demographic groups, catch their attention, explain the purpose of the drive, and prescreen them to ensure eligibility. Recruiters then secure informed consent, educating registrants about the stem cell donation process, the risks involved, the right to withdraw, and donor-patient anonymity. Recruiters subsequently ask registrants to register by providing their contact/demographic information, completing a health questionnaire, and signing a consent form. Recruiters also guide registrants to provide a tissue sample (e.g., buccal swab) for typing. Finally, recruiters reconcile completed registration kits and prepare them for shipment to the donor registry. Data are presented demonstrating the effectiveness of stem cell drives employing this protocol on recruitment of the most-needed donor demographics and of quality donors. CONCLUSION: This protocol incorporates best practices for unrelated donor recruitment. It is relevant to donor recruitment organizations worldwide seeking to improve their recruitment efforts and standardize registrant experience.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".