Patient and Caregiver Perceptions on the Allocation Process and Waitlist, and Accepting a Less-Than-Ideal Kidney: A Canadian Survey
Bibliographic record
Abstract
Background: Transplanting less-than-ideal (LTI) kidneys could help optimize organ utilization, but little is known about how patients and caregivers perceive the allocation process, waitlist, or LTI kidneys. Objective: To explore the perspectives of patients and caregivers on the Canadian kidney transplant allocation process, waitlist, and LTI kidneys. Design: Electronic survey. Setting: Canada. Patients: Transplant recipients, candidates, and caregivers. Methods: A bilingual electronic national survey was administered from January to March 2024. The questionnaire contained sections on demographics, perceptions of organ allocation and acceptance, LTI kidneys, and educational preferences. Descriptive analysis was performed. Results: Two hundred fifty-one responses were analyzed, including patients (63%, n = 159), and caregivers (37%, n = 92), from 11 provinces and territories. Three-quarters (74%, n = 186) understood how patients are placed on the waiting list, and 65% (n = 162) understood how donor kidneys are allocated, but 72% (n = 181) and 68% (n = 171) wanted more information about the waitlist and donor kidney allocation criteria, respectively. Approximately 20% felt that the waitlist and allocation processes were not transparent. Awareness about the option to refuse a deceased donor kidney offer was high (69%, n = 174), yet nearly half of respondents (46%, n = 115) expressed concern about being disadvantaged if an offer for a deceased donor kidney was refused. One-third of participants (33%, n = 83) were open to accepting an LTI kidney. Limitations: Compared to the general population, more study participants were white, and the majority were educated and financially at ease. This limits the generalizability of the results. Conclusion: Enhanced communication is required to improve transparency and information about the allocation system and waitlist in Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".