Contact Between Living Anonymous Donors and Recipients: Current Practice and Patient Education Guidelines.
Bibliographic record
Abstract
PURPOSE: This presentation will review current practice, policies, and ethical dilemmas regarding contact between Living Anonymous Donors (LADs) and Recipients and will propose guidelines including patient education brochures for LADs and Recipients. BACKGROUND: Transplant Recipients sometimes request help to express gratitude to their liver or kidney or kidney paired exchange LADs. LADs sometimes wish to communicate with and to meet their Recipients before and/or after donation. Transplant programmes may be uncertain about what kind(s) of contact they should enable between LADs and Recipients and how to facilitate this while protecting confidentiality. Organ procurement organizations often support communication via anonymous cards/letters or facilitate meetings between Recipients and families of Deceased Donors. When Living Donors (LDs) and Recipients know each other, Recipients can thank their LDs in person, and LDs may benefit from seeing the Recipient's health improve post-transplant. Should transplant programmes extend this benefit by facilitating communication and meetings between LADs and Recipients? How should written communication be screened and who should do this? Face to face meetings may result in disappointment, unrequited wishes for continued contact, or problematic behaviours. Transplant programmes are unable to protect LADs and Recipients from unwanted consequences of contact after they have met. METHODS: A review of relevant literature was completed and the practices and experiences of centres in North America and Europe were summarized. Ethically challenging case scenarios and current practice were reviewed by a multidisciplinary working group at the Multi-Organ Transplant Program, Toronto General Hospital, University Health Network. RESULTS: Through a series of consensus steps, a standard operating procedure and practice guidelines were developed to ensure consistent practice and to clarify responsibilities of involved parties. Bioethics and Social Work also created guidelines for LADs and Recipients via patient education brochures. CONCLUSION: We conclude that programmes that facilitate contact between LADs and Recipients should develop policies and standard operating procedures. We propose ways to inform LADs and Recipients about the options for contact and specific guidelines for written contact via anonymous cards/letters.
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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 | 0.048 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".