Canadian Kidney Transplant Professionals’ Perspectives on Precision Medicine and Molecular Matching in Kidney Allocation
Bibliographic record
Abstract
Background: Antibody-mediated rejection is an important cause of kidney transplant loss. A new strategy requiring application of precision medicine tools in transplantation considers molecular compatibility between donors and recipients and holds the promise of improved immunologic risk, preventing rejection and premature graft loss. The objective of this study was to gather Canadian transplant professionals' perspectives on molecular compatibility in kidney transplantation. Methods: Seventeen Canadian transplant professionals (14 nephrologists, 2 nurses, and 1 surgeon) participated in semistructured interviews in 2021. The interviews were digitally recorded, transcribed, and analyzed using the qualitative description approach. Results: Participants identified fair access to transplantation as the most important principle in kidney allocation. Molecular compatibility was viewed as a promising innovation. However, participants were concerned about increased waiting times, negative impact on some patients, and potential problems related to the adequacy of information explaining this new technology. To mitigate the challenges associated with molecular matching, participants suggested integrating a maximum waiting time for molecular-matched kidneys and expanding the program nationally/internationally. Conclusions: Molecular matching in kidney transplantation is viewed as a promising technology for decreasing the incidence of antibody-mediated rejection and improving graft survival. Further studies are needed to determine how to ethically integrate this technology into the kidney allocation algorithm.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.015 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".