Ethical Advocacy for Organ Donation by Transplant Providers
Bibliographic record
Abstract
The majority of individuals who read articles in Links are well aware of the pressing need to increase organ donation. I would like to focus on pediatric heart recipients, a group with the highest waiting list mortality of solid organ transplantation in the US.1 As transplant providers, we are expected to be strong advocates for recipients. Speaking with my transplant care provider hat on, I am concerned that active advocacy for organ donation has been muted perhaps by our caution to avoid conflicts of interests. In specific, UNOS Policy 3.4.1 updated in 2010 entitled “Avoidance of Conflicts of Interest ” states that “ … neither the attending physician of the decedent at death nor the physician who determines the time of the decedent’s death may participate in the operative procedure for removing or transplanting an organ from the decedent …” Lori West and her team reported in 20012, the outcomes of listing infants for the next available heart independent of their blood type. By adopting this ABO-“independent ” strategy, waiting times and death for infant recipients have dramatically fallen in Canada. ABO-“incompatible ” listing was introduced for infants under 1 year old in the US. The blood group O recipients, who were most disadvantaged with blood group compatible listing, have benefited coming to transplant by 30 days after ABO-I listing with an overall reduction in waiting time by as much as 45 % (i.e., 87 to 48 days).3 Importantly, we
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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 | 0.061 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.036 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".