400.4: Deceased Kidney Donor Acceptance Criteria: A Survey of Canadian Transplant Nephrologists, Surgeons and Urologists
Notice bibliographique
Résumé
Introduction: Kidney transplantation provides a quality of life and survival advantage for patients with end-stage kidney disease (ESKD) relative to remaining on dialysis. However, the number of patients on the transplant waitlist is steadily increasing relative to the number of available kidney donors. Despite this, while Canadian organ discard rates are not available, the proportion of discarded kidneys in the United States has paradoxically increased over time. While there is an abundance of literature regarding predictors of organ decline in the United States, to date, there are no data regarding the rate or rationale for deceased donor decline in Canada. Methods: We performed an online survey (July 22-Oct 4, 2021) to assess deceased donor acceptance practices of Canadian transplant nephrologists, surgeons and urologists. Surveys were distributed by email to members of the Canadian Transplant Society. Surveys consisted of 3 sections of increasing donor complexity and respondents were asked whether they would accept or decline a hypothetical donor presented in the question stem, assuming there was a suitable recipient. The proportion of respondents completing the survey was determined, as were overall and donor scenario-specific acceptance rates amongst those providing responses. Results: A total of 81 respondents accessed the survey from 19 centers and seven provinces across Canada (all provinces with at least one transplant center). A total of 72 respondents (88.9%) answered at least one question and 16 respondents (22.2%) did not complete the survey. Donor acceptance rates for Sections 1 and 2 are demonstrated in Figure 2. Overall acceptance rates were highest for the younger (40 years) donor scenarios and when the donor was NDD vs. DCD. The most pronounced drop in acceptance rates for all donor scenarios was between a non-dialysis dependent donor with recovering AKI (92% acceptance) and a donor with AKI requiring dialysis and a biopsy demonstrating ATN but no coagulative necrosis (20% acceptance). Acceptance rates for Section 3 are demonstrated in Figure 3. The most pronounced drop in acceptance rates for all donor scenarios was between a deceased donor with comorbidities but no CKD (85% acceptance) and a donor with CKD and a biopsy demonstrating 3 out of 12 glomeruli sclerosed, but no arterial hyalinosis (25% acceptance). Overall, advanced donor age, DCD donor status, AKI, CKD and comorbidity burden were all associated with an increased risk of deceased donor non-acceptance. Conclusions: Given relatively high rates of donor decline and apparent heterogeneity in acceptance decisions, Canadian transplant specialists may benefit from additional education regarding the benefits achieved from even medically complex or “marginal” kidney donors for appropriate candidates relative to remaining on dialysis on the transplant waitlist.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».