A RAND-Modified Delphi on Key Indicators to Measure the Efficiency of Living Kidney Donor Candidate Evaluations
Bibliographic record
Abstract
Background and objectives Many patients, providers, and potential living donors perceive the living kidney donor evaluation process to be lengthy and difficult to navigate. Design, setting, participants, & measurements We sought consensus on key terms and process and outcome indicators that can be used to measure how efficiently a transplant center evaluates persons interested in becoming a living kidney donor. Using a RAND-modified Delphi method, 77 participants (kidney transplant recipients or recipient candidates, living kidney donors or donor candidates, health care providers, and health care administrators) completed an online survey to define the terms and indicators. The definitions were then further refined during an in-person meeting with ten stakeholders. Results We identified 16 process indicators ( e.g. , average time to evaluate a donor candidate), eight outcome indicators ( e.g. , annual number of preemptive living kidney donor transplants), and two measures that can be considered both process and outcome indicators ( e.g. , average number of times a candidate visited the transplant center for the evaluation). Transplant centers wishing to implement this set of indicators will require 22 unique data elements, all of which are either readily available or easily collected prospectively. Conclusions We identified a set of indicators through a consensus-based approach that may be used to monitor and improve the performance of a transplant center in how efficiently it evaluates persons interested in becoming a living kidney donor.
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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 | 0.225 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".