The Roles of a Bioethicist on an Organ Transplantation Service
Bibliographic record
Abstract
Organ transplantation centers have expanded and increased in the last 20 years as transplant recipient outcomes have improved steadily and transplantation has moved from experimentation to treatment of choice for several indications. Transplantation presents difficult ethical and legal challenges for the transplant community and society. These include declarations of death, consent to donation and allocation of a scarce societal resource, i.e. transplantable organs. Policy and practice reflect the law, societal beliefs and prevailing values. A bioethicist contributes to a transplant team by clarifying values held by various stakeholders or embodied in decisions and policies, conducting clinical consultations, developing and interpreting policy and researching the ethics of innovations for rationing and increasing available supply of organs for transplantation. The bioethicist's interdisciplinary education, preparation, experience and familiarity with ethics, law, sociology and philosophy and skills of mediation, communication and ethical analysis contribute to addressing and resolving many issues in transplantation. This paper outlines the various roles of a bioethicist on a transplantation service, using case examples to illustrate some of the ethical issues.
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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 | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.045 | 0.047 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.024 | 0.032 |
| Insufficient payload (model declined to judge) | 0.012 | 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".