The Efficiency of Evaluating Candidates for Living Kidney Donation: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: The process of evaluating candidates for living kidney donation can be inefficient. A structured review of existing information on this topic can provide a necessary foundation for quality improvement. METHODS: We conducted a scoping review to map the published literature to different themes related to an efficient donor candidate evaluation. We reviewed the websites of living donor programs to describe information provided to candidates about the nature and length of the evaluation process. RESULTS: We reviewed of 273 published articles and 296 websites. Surveys of living donor programs show variability in donor evaluation protocols. Computed tomography (a routinely done test for all successful candidates) may be used to assess split renal volume instead of nuclear renography when the 2 kidneys differ in size. Depending on the candidate's estimated glomerular filtration rate, a nuclear medicine scan for measured glomerular filtration rate may not be needed. When reported, the time to complete the evaluation varied from 3 months to over a year. The potential for undesirable outcomes was reported in 23 studies, including missed opportunities for living donation and/or preemptive transplants. According to living donor websites, programs generally evaluate 1 candidate at a time when multiple come forward for assessment, and few programs describe completing most of the evaluation in a single in-person visit. CONCLUSIONS: Data on the efficiency of the living donor evaluation are limited. Future efforts can better define, collect, and report indicators of an efficient living donor evaluation to promote quality improvement and better patient outcomes.
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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 | 0.022 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".