Canadian Kidney Transplant Recipients’, Transplant Candidates’, and Caregivers’ Perspectives on Precision Medicine and Molecular Matching in Kidney Allocation: A Qualitative Analysis
Bibliographic record
Abstract
Background: Antibody-mediated rejection (AMR) 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. Objective: The objective of this study was to gather patients' and caregivers' perspectives on molecular compatibility in kidney transplantation. Design: Individual semi-structured interviews. Setting: The Centre hospitalier de l'Université de Montréal (CHUM) and McGill University Health Centre (MUHC) kidney transplant programs. Participants: Kidney transplant candidates, kidney transplant recipients, and caregivers. Methods: Twenty-seven participants took part in semi-structured interviews between July 2020 and November 2021. The interviews were digitally recorded, transcribed, and analyzed using the qualitative description approach. Results: Participants had different levels of knowledge about the kidney allocation process. They expressed trust in the system and healthcare professionals. They indicated that a fair organ allocation system should strive to maximize graft survival as it would decrease the demand for deceased donor kidneys and allow more patients to access transplantation. Molecular matching and precision medicine were seen as important improvements in the kidney transplant allocation process given their potential to improve graft survival and decrease the need for retransplantation. However, participants were concerned about increased waiting times that may negatively impact some patients upon implementation of molecular matching. To address these concerns, participants suggested integrating safeguards in the form of maximum waiting time for molecularly matched kidneys. Limitations: This study was conducted in the province of Quebec most of the participants were white and highly educated. Consequently, the results could not be generalizable to other populations, including ethnic minorities. Conclusions: Molecular matching and precision medicine are viewed as promising technologies for decreasing the incidence of AMR and improving graft survival. However, further studies are needed to determine how to ethically integrate this technology into the kidney allocation scheme. Trial registration: Not registered.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".