How the websites of high‐volume US centers address the risks of living kidney donation
Bibliographic record
Abstract
BACKGROUND: The websites of US transplant centers may be a source of information about the renal risks of potential living kidney donors. METHODS: To include only likely best practices, we surveyed websites of centers that performed at least 50 living donor kidney transplants per year. We tabulated how risks were conveyed regarding loss of eGFR at donation, the adequacy of long-term ESRD risk data, long-term donor mortality, minority donor ESRD risk, concerns about hyperfiltration injury versus the risk of end-stage kidney diseases, comparisons of ESRD risks in donors to population risks, the increased risks of younger donors, an effect of the donation itself to increase risk, quantifying risks over specific intervals, and a lengthening list of small post-donation medical risks and metabolic changes of uncertain significance. RESULTS: While websites had no formal obligation to address donor risks, many offered abundant information. Some conveyed OPTN-mandated requirements for counseling individual donor candidates. While actual wording often varied, there was general agreement on many issues. We occasionally noted clear-cut differences among websites in risk characterization and other outliers. CONCLUSIONS: The websites of the most active US centers offer insights into how transplant professionals view living kidney donor risk. Website content may merit further study.
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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 | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".