The kidney evaluation of living kidney donor candidates: US practices in 2017
Bibliographic record
Abstract
We surveyed US transplant programs to assess practices used to assess kidney health in living kidney donor candidates in 2017; the response rate was 31%. In this report, we focus on the kidney; a companion piece focuses on the metabolic and cardiovascular aspects of candidate evaluation. Compared to 2005, programs have become more stringent in accepting younger candidates and less stringent in accepting older candidates. The 24-hour creatinine clearance remains the mainstay for kidney function assessment, with 74% continuing to use a value below 80 mL/min/1.73 m2 for exclusion and 22% using age-based criteria. ApoL1 genotyping is obtained routinely or selectively by 45%, half of which use the high-risk genotype as an absolute exclusion criterion. For history of symptomatic stones, 49% accept if there is no current radiographic evidence of stones and urine profile is low risk, 80%-95% consider candidates with unilateral asymptomatic stones, but only 33%-48% consider if stones are bilateral. In addition, 14% use the risk assessment tool developed by Grams et al routinely for decision-making, and 42% use it sometimes. Also, 57% reported not having yet determined a risk threshold for acceptable postdonation risk above which candidates are excluded. Contemporary practice variation underscores the need for better evidence to guide the donor selection process.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".