Transplant professionals vary in the long-term medical risks they communicate to potential living kidney donors: an international survey
Bibliographic record
Abstract
BACKGROUND: Discussing long-term medical risks with potential living donors is a vital aspect of informed consent. We considered whether there are global practice variations in the information communicated to potential living kidney donors. METHODS: Transplant professionals participated in a survey to determine which long-term risks are communicated to potential living kidney donors. Self-administered questionnaires were distributed in person and by electronic mail. RESULTS: We surveyed 203 practitioners from 119 cities in 35 different countries. Sixty-three percent of participants were nephrologists, and 27% were surgeons. Risks of hypertension, proteinuria or kidney failure requiring dialysis were frequently discussed (usually over 80% of practitioners discussed each medical condition). However, many practitioners do not believe these risks are increased after donation, with surgeons being less convinced of long-term sequelae compared with nephrologists (P < 0.01). About 30% of practitioners discuss long-term risks of premature cardiovascular disease or death with potential donors. CONCLUSIONS: Transplant professionals vary in the long-term risks they communicate to potential donors. Improving consensus will enhance decision-making, and emphasize best practices which maintain good, long-term donor health.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".