Canadian transplant nephrologists’ perspectives on the decision‐making process for accepting or refusing a kidney from a deceased organ donor
Bibliographic record
Abstract
BACKGROUND: Kidney transplantation is the best treatment for patients with end-stage renal disease. The decision to accept a kidney from a deceased donor can be a difficult one, especially when organs from high KDPI (>85%) donors are offered. This study aims to capture the perspectives of transplant nephrologists (TNs) on the decision-making process when an organ is offered. METHODS: Fifteen Canadian TNs took part in semi-structured interviews between December 2017 and April 2018. The interviews were digitally recorded, transcribed, and analyzed using the thematic analysis method. RESULTS: The decision to accept a deceased-donor kidney offer is a medical one for the participants. However, transplant candidates could be involved when the offered kidney is from a donor with a KDPI >85% or increased infectious risk donor. The TNs' past experience, comprehensive data on the donor, and education of the transplant candidate could facilitate the decision-making process. A decision aid could also facilitate the decision-making process, but different concerns should be addressed. CONCLUSION: Although accepting a deceased-donor organ offer is often viewed as an opportunity for shared decision-making, participants in this study viewed the decision to accept or refuse an offer as a medical decision with little room for patient participation.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".