Organ Donation in Croatia: The Importance of a National Champion, a Comprehensive Plan, and International Collaborations
Bibliographic record
Abstract
The Republic of Croatia is a global leader in organ donation and transplantation despite having fewer resources and more modest healthcare expenditures than other countries in the European Union. The results of an extensive literature review were combined with expert input in an iterative multi-step data collection and evaluation process designed to assess trends in Croatian organ donation and transplantation and identify key elements, policy changes, and drivers of the system that have contributed to its success. Multiple sources of evidence were used in this study, including primary documents, national and international transplantation reports, and insights from critical informants and content experts. The results highlight several key organizational reforms that have substantially improved the performance of the Croatian transplant program. Our findings emphasize the importance of strong central governance led by an empowered national clinical leader acting under the direct auspices of the Ministry of Health and a comprehensive and progressive national plan. The Croatian transplant system is notable for its integrated approach and efficient manner of managing scarce health resources. Collectively, the results suggest that Croatia has become nearly self-sufficient due to its systematic implementation of the guiding principles for organ donation and transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".