Public Appeals Challenging Criteria for Pediatric Organ Transplantation
Bibliographic record
Abstract
In this article, I review the ethical issues that arise in the allocation of deceased-donor organs to children and young adults. By analyzing the public media cases of Sarah Murnaghan, Amelia Rivera, and Riley Hancey, I assess whether public appeals to challenge inclusion and exclusion criteria for organ transplantation are ethical and under which circumstances. The issues of pediatric allocation with limited evidence and candidacy affected by factors such as intellectual disability and marijuana use are specifically discussed. Finally, I suggest that ethical public advocacy can coexist with well-evidenced transplant allocation if and when certain conditions (morally defensible criteria, expert evidence, nonprioritization of the poster child, and greater advocacy for organ transplantation in general) are met.
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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 | 0.041 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".