Notice bibliographique
Résumé
Abstract Patients with a variety of blood cancers and metabolic diseases may require a stem cell transplant as part of their treatment. However, 70% of patients do not have a suitable genetic match in their family. Stem cell donor-databases are used to match potential unrelated donors to patients worldwide. In Canada, individuals aged 17-35 years can register as donors online or at a stem cell drive where they provide consent and a tissue sample (buccal-swab) for Human Leukocyte Antigen (HLA) allele typing. To date, no guidelines have been published to recommend a process for stem cell donor recruitment at drives. Here, I outline a Canadian approach to stem cell drive design, which features evidence-based strategies to identify and recruit the most-needed stem cell donors and to minimize donor ambivalence and withdrawal from the registry. This model of stem cell drive design includes five stations: pre-screening, informed consent, registration, swabbing, and reconciliation. Registrant confidentiality and privacy is maintained at each station, and quality control is emphasized throughout. Registrants are first pre-screened to ensure donor eligibility. Recruiters at the prescreening station target the most-needed stem cell donors according to the literature: young, healthy, and ethnically-diverse males. Non-optimal and ineligible registrants are redirected to help out in other ways. Recruiters then explain the principles of stem cell donation and educate registrants about the stem cell donation process. Next, registrants proceed to the informed consent station, which is designed to meet the requirements of the World Marrow Donor Association's suggested procedures for procurement of informed consent at time of recruitment (2003). Here, recruiters hand registrants an information pamphlet, and explain blood and marrow stem cell collection procedure diagrams. The risks of donation are outlined, and registrants are informed of their right to withdraw at any time and about donor and patient anonymity. Registrants are subsequently guided through registration, where they provide their contact/demographic information, complete a health questionnaire, and sign a consent form to join the registry. Recruiters at this station error check registrants' forms to ensure correct completion, and educate registrants about the data collection, storage, usage, and confidentiality. Following registration, registrants proceed to swabbing, where they swab their cheeks to provide a tissue/DNA sample. Recruiters at this station affix barcode stickers to each swab kit component. While registrants swab their cheeks, volunteers perform an informed consent checkpoint by asking registrants if they understand the donation process, the risks involved, and the right to withdraw at any time. Finally, registrants visit reconciliation, where their paperwork is error checked again, their understanding of the donation process is assessed a final time to verify informed consent, and their kit processed for shipping. In summary, the five-station approach to stem cell drive design outlined in this presentation represents a new model for effective stem cell donor recruitment. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.
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 | 0,007 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,011 | 0,008 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,002 |
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 ».