Trafficking in Human Beings for the Purpose of Organ Removal and the Ethical and Legal Obligations of Healthcare Providers
Bibliographic record
Abstract
Physicians and other health care professionals seem well placed to play a role in the monitoring and, perhaps, in the curtailment of the trafficking in human beings for the purpose of organ removal. They serve as important sources of information for patients and may have access to information that can be used to gain a greater understanding of organ trafficking networks. However, well-established legal and ethical obligations owed to their patients can create challenging policy tensions that can make it difficult to implement policy action at the level of the physician/patient. In this article, we explore the role-and legal and ethical obligations-of physicians at 3 key stages of patient interaction: the information phase, the pretransplant phase, and the posttransplant phase. Although policy challenges remain, physicians can still play a vital role by, for example, providing patients with a frank disclosure of the relevant risks and harms associated with the illegal organ trade and an honest account of the physician's own moral objections. They can also report colleagues involved in the illegal trade to an appropriate regulatory authority. Existing legal and ethical obligations likely prohibit physicians from reporting patients who have received an illegal organ. However, given the potential benefits that may accrue from the collection of more information about the illegal transactions, this is an area where legal reform should be considered.
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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 | 0.046 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.040 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".