Kidney transplant candidates’ and recipients’ perspectives on the decision‐making process to accept or refuse a deceased donor kidney offer: Trust and graft survival matter
Bibliographic record
Abstract
BACKGROUND: The decision to accept a kidney from a deceased donor can be a difficult one. This study aims to capture the perspectives of transplant candidates (TCs) and kidney transplant recipients (KTRs) on the decision-making process when a deceased kidney is offered. METHODS: We conducted six focus groups with KTRs and TCs. The content of the focus groups was analyzed using the qualitative thematic method. RESULTS: KTRs reported that the experience of being offered a kidney could be difficult because of the circumstances of the offer and unpreparedness to participate in the discussion. Both KTRs and TCs trusted the medical expertise. Age and having experience with dialysis could influence the decision to accept an offer. In order to engage in the discussion, patients wanted to obtain estimates of expected graft survival. Patients did not express interest for a web-based calculator for patient use, but expected transplant physicians to summarize and explain the information that would impact graft survival time. CONCLUSION: TCs and KTRs wanted to be involved in the decision to accept a deceased donor kidney. Tools that can help physicians communicate the risks and benefits of accepting an offer could improve patient participation in the decision-making process.
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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 | 0.001 | 0.000 |
| 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) | 0.002 | 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".