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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".