Inclusive practices for safe and equitable donor assessment
Bibliographic record
Abstract
Abstract Background: Guidance is needed to optimize the recruitment, verification typing (VT) and workup of donors from vulnerable populations and to overcome structural barriers to donation. Purpose: In 2022, the World Marrow Donor Association (WMDA) Donor Medical Suitability Committee set out to advance health equity in donor suitability guidance (published to https://share.wmda.info/display/LP/Donor+Suitability+Pages+index ). Our goals were to harmonize global practices for donor assessments, and concurrently advance health equity and donation safety for patients and donors. Expanding on this work, here, we report the development of recommendations on inclusive practices for safe and equitable donor assessments. Methods: A project group was assembled including representation from donor registries worldwide, specialists in stem cell transplantation, donor care and follow-up, and cellular therapy, and healthcare providers with lived experience with and/or expertise caring for vulnerable populations, across the intersectionality of race, ethnicity, sex, gender identity, sexual orientation, socioeconomic status, and disability. The group met regularly to develop consensus recommendations, as well as tools to guide implementation. Guidance developed focused on potential unrelated peripheral blood stem cell, bone marrow, and maternal cord blood donors, with most recommendations also being applicable to related allograft, autologous stem cell, and cell therapy product donors. Results: We developed a series of recommendations for inclusive practices for safe and equitable assessment of donors from vulnerable populations. Recommendations emphasized that health equity should be prioritized alongside donation safety, and provided guidance on donor/ transplant center communication with donors, donor health history questionnaire design, deferral criteria at registration, VT, or workup, reporting requirements for donor centers to transplant centers and to recipients (balancing clinical decision making/patient safety with donor privacy/confidentiality), and equity in laboratory testing and evaluation. Specific recommendations focused on donors who are racialized, transgender or non-binary, facing social (e.g. language), cultural, or financial barriers, living with disabilities or mental health challenges, or those who are living with HIV, taking HIV pre- or post-exposure prophylaxis, or have a history of high-risk sexual behavior, non-prescription injection drug use, incarceration, or sex work. Tools developed to guide implementation included inclusive donor screening questionnaires, scripts, workflows, and training to guide collection, reporting, and use of sensitive donor data, and algorithms to guide clinical decision making for vulnerable populations. Conclusions: These guidelines and tools will support stakeholders across transplantation and cellular therapy, including donor and transplant centers and all medical teams involved in donor assessments, to advocate for donation policies and practices which uplift, include, support, and empower donors from marginalized groups. Implementing these recommendations will help dismantle structural barriers to donation, improve donor well-being and enhance donation experience, and advance a more inclusive healthcare system for donors from vulnerable populations.
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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 | 0.245 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".