Transplant Professionals’ Proposals for the Implementation of an Altruistic Unbalanced Paired Kidney Exchange Program
Bibliographic record
Abstract
BACKGROUND: Kidney recipients in the O blood group are at a disadvantage in kidney exchange programs (KEPs) because they can only receive an organ from O blood group donors. A way to remedy this unfair situation is through altruistic unbalanced paired kidney exchange (AUPKE) where a compatible pair (CP) consisting of an O donor and a non-O recipient is invited to participate in a KEP. There is no established AUPKE program in Canada. The aim of this study was to gather transplant professionals' views on the conditions necessary for the implementation of an AUPKE program. METHODS: Nineteen Canadian transplant professionals took part in semistructured interviews. The content of these interviews was analyzed using a qualitative data analysis method. RESULTS: Respondents' recommendations focused on the following: (i) the logistics of AUPKE (e.g., not delaying the transplantation for the CP, retrieving organs locally, providing a good quality organ to the CP, and maintaining anonymity); (ii) the transplantation teams (e.g., establishing a consensus among members and ensuring sufficient resources); (iii) information provided to CPs; and (iv) research (e.g., looking into all transplant options for O recipients, studying all potential impacts of KEPs and AUPKE). CONCLUSION: The respondents in our study made the following recommendations for the implementation of an AUPKE program: (i) CPs should not be disadvantaged, (ii) measures should be taken to ensure that all transplant team members agree to participate and that there are sufficient resources for implementation, (iii) comprehensive information should be provided to the CP, and (iv) further research is needed on AUPKE.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".