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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".