Ten Lessons From the Spanish Model of Organ Donation and Transplantation
Bibliographic record
Abstract
The organ donation and transplantation program in Spain has long been considered the gold standard worldwide. An in-depth understanding of the Spanish program may promote the development and reform of transplant programs in other countries. Here, we present a narrative literature review of the Spanish organ donation and transplantation program supplemented by expert feedback and presented according to a conceptual framework of best practices in the field. Core features of the Spanish program include its three-tiered governing structure, close and collaborative relationships with the media, dedicated professional roles, a comprehensive reimbursement strategy, and intensive tailored training programs for all personnel. Several more sophisticated measures have also been implemented, including those focused on advanced donation after circulatory death (DCD) and expanded criteria for organ donation. The overall program is driven by a culture of research, innovation, and continuous commitment and complemented by successful strategies in prevention of end-stage liver and renal disease. Countries seeking ways to reform their current transplant systems might adopt core features and may ultimately aspire to include the aforementioned sophisticated measures. Countries intent on reforming their transplant system should also introduce programs that support living donation, an area of the Spanish program with potential for further improvement.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| 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".