Attitudes to Sharing Personal Health Information in Living Kidney Donation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In living kidney donation, transplant professionals consider the rights of a living kidney donor and recipient to keep their personal health information confidential and the need to disclose this information to the other for informed consent. In incompatible kidney exchange, personal health information from multiple living donors and recipients may affect decision making and outcomes. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We conducted a survey to understand and compare the preferences of potential donors (n = 43), potential recipients (n = 73), and health professionals (n = 41) toward sharing personal health information (in total 157 individuals). RESULTS: When considering traditional live-donor transplantation, donors and recipients generally agreed that a recipient's health information should be shared with the donor (86 and 80%, respectively) and that a donor's information should be shared with the recipient (97 and 89%, respectively). When considering incompatible kidney exchange, donors and recipients generally agreed that a recipient's information should be shared with all donors and recipients involved in the transplant (85 and 85%, respectively) and that a donor's information should also be shared with all involved (95 and 90%, respectively). These results were contrary to attitudes expressed by transplant professionals, who frequently disagreed about whether such information should be shared. CONCLUSIONS: Future policies and practice could facilitate greater sharing of personal health information in living kidney donation. This requires a consideration of which information is relevant, how to put it in context, and a plan to obtain consent from all concerned.
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 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 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".