400.4: Deceased Kidney Donor Acceptance Criteria: A Survey of Canadian Transplant Nephrologists, Surgeons and Urologists
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".