Inclusive practices for safe and equitable donor assessment
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,245 | 0,265 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,006 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,009 |
| Communication savante | 0,011 | 0,013 |
| Science ouverte | 0,008 | 0,029 |
| Intégrité de la recherche | 0,008 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».