Lessons From the Portuguese Solid Organ Donation and Transplantation System: Achieving Success Despite Challenging Conditions
Bibliographic record
Abstract
Over the past two decades, Portugal has become one of the world leaders in organ donation and transplantation despite significant financial constraints. This study highlights how Portugal achieved success in organ donation and transplantation and discusses how this information might be used by other countries that are seeking to reform their national programs. To accomplish this goal, we performed a narrative review of relevant academic and grey literature and revised our results after consultation with two national experts. Our findings were then synthesized according to a conceptual framework for organ donation and transplantation programs. Our results revealed several key strategies used by the Portuguese organ donation and transplantation program, including collaboration with Spain and other European nations, a focus on tertiary prevention, and sustained financial commitment. This report also explores how cooperative efforts were facilitated by geographical, governmental, and cultural proximity to Spain, a world leader in organ donation and transplantation. In conclusion, our review of the Portuguese experience provides insight into the development of organ donation and transplantation systems. However, other countries seeking to reform their national transplant systems will need to adapt these policies and practices to align with their unique cultures and contexts.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".