Disparities in Deceased Donor Kidney Offer Acceptance: A Survey of Canadian Transplant Nephrologists, General Surgeons and Urologists
Bibliographic record
Abstract
Background: Significant variability in organ acceptance thresholds have been demonstrated across the United States, but data regarding the rate and rationale for kidney donor organ decline in Canada are lacking. Objective: To examine decision making regarding deceased kidney donor acceptance and non-acceptance in a population of Canadian transplant professionals. Design: A survey study of theoretical deceased donor kidney cases of increasing complexity. Setting: Canadian transplant nephrologists, urologists, and surgeons making donor call decisions responding to an electronic survey between July 22 and October 4, 2022. Participants: Invitations to participate were distributed to 179 Canadian transplant nephrologists, surgeons, and urologists through e-mail. Participants were identified by contacting each transplant program and requesting a list of physicians who take donor call. Measurements: Survey respondents were asked whether they would accept or decline a given donor, assuming there was a suitable recipient. They were also asked to cite reasons for donor non-acceptance. Methods: Donor scenario-specific acceptance rates (total acceptance divided by total number of respondents for a given scenario and overall) and reasons for decline were determined and presented as a percentage of the total cases declined. Results: -value < .001. There was an increased risk of non-acceptance with advancing age, donation after cardiac death, acute kidney injury, chronic kidney disease, and comorbidities. Limitations: As with any survey, there is the potential for participation bias. In addition, this study examines donor characteristics in isolation, however, asks respondent to assume there is a suitable candidate available. In reality, whenever donor quality is considered, it should be considered in the context of the intended recipient. Conclusion: In a survey of increasingly medically complex deceased kidney donor cases, there was significant variability in donor decline among Canadian transplant specialists. 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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".