Transplantation in the Context of Migration and Refugees: A Summary of the DICG and TTS Ethics Committee Workshop, Buenos Aires, Argentina, September 2022
Bibliographic record
Abstract
Global conflicts and humanitarian crises have resulted in an unprecedented number of refugees and migrants. This challenges the limited resources of health care systems and jeopardizes the availability of transplant care for these deserving migrants and refugees. This was the basis for a workshop held during the Congress of the Transplantation Society (Buenos Aires, 2022). We elaborate on the proceedings of the workshop entitled "Transplantation in the Context of Migration and Refugees," organized by the Ethics Committee of The Transplantation Society and Declaration of Istanbul Custodian Group. Transplant providers from around the world shared strategies of how each region has responded to providing access to care for refugees and migrants in need of transplant services. The potential exploitation of this vulnerable group leading to illicit organ removal was addressed for each region. The Transplantation Society, Declaration of Istanbul Custodian Group, and global transplant community should continue to focus on the status of refugees and migrants and collaborate on strategies to provide access to transplant care for this deserving population. Global cooperation will be essential to provide vigilant oversight to prevent exploitation of this vulnerable population.
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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 | 0.037 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".