Discovering Misattributed Paternity in Living Kidney Donation
Bibliographic record
Abstract
When evaluating a living kidney donor and recipient with a father-child relationship, it may be discovered that the two are not biologically related. We analyzed data from the United Network for Organ Sharing and the Canadian Organ Replacement Registry to determine how frequently this occurs. We surveyed 102 potential donors, recipients, and transplant professionals for their opinion on whether such information should be disclosed to the donor-recipient pair. We communicated with transplant professionals from 13 Canadian centers on current practices for handling this information. In the United States and Canada, the prevalence of father-child living kidney donor-recipient pairs with less than a one-haplotype human leukocyte antigen match (i.e., misattributed paternity) is between 1% and 3%, or approximately 0.25% to 0.5% of all living kidney donations. Opinions about revealing this information were variable: 23% strongly favored disclosure; whereas, 24% were strongly opposed to it. Current practices are variable; some centers disclose this information, whereas others do not. Discovering misattributed paternity in living donation is uncommon but can occur. Opinions on how to deal with this sensitive information are variable. Discussion among transplant professionals will facilitate best practices and policies. Strategies adopted by some centers can be considered.
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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 | 0.029 | 0.103 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".