Transplant Candidates and Substance Use: Adopting Rational Health Policy for Resource Allocation
Bibliographic record
Abstract
Organ transplant candidates are often denied life saving organs on account of their medical marijuana drug use. Individuals who smoke medicinal marijuana are typically classified as substance abusers, and ultimately deemed ineligible for transplantation, despite their receipt of the drug under a physician's supervision and prescription. However, patients who smoke cigarettes or engage in excessive alcohol consumption are routinely considered for placement on the national organ transplant waiting list. Transplant facilities have the freedom to regulate patient selection criteria with minimal oversight. As a result, the current organ allocation system in the United States is rife with inconsistencies and results in disparities in allocation decisions. This Article reviews the history and underlying rationale of organ allocation in the United States and the National Organ Transplant Act. It then examines ill-founded policies regarding transplant candidates who present issues of substance "abuse" compared with substance "use," and the resulting disparities in waiting-list criteria. In response, a model rule for a national set of patient selection guidelines is provided. Definitions of terms, distinctions regarding proper patient classification, and protocols for a second chance policy to be used in the event of a relapse among wait-listed patients are addressed. Finally, stipulations that require designated abstention periods as well as random drug screenings in relation to subsequent relisting are also included. This policy distinguishes between candidates who present issues of substance use versus substance abuse. The use of such a model allocation policy will promote equity and scientific bases in the organ allocation process.
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.018 | 0.014 |
| 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".