Donor Heart Allocation Systems in Europe A Scientific Statement of the Heart Failure Association of the ESC
Bibliographic record
Abstract
Heart transplantation remains the gold standard for treatment of most patients with advanced heart failure (HF), but despite improvements of organ recovery and utilization, donor heart scarcity remains a critically limiting factor. Detailed heart allocation systems (HASs) are in place to ensure use of organs for appropriate candidates, In Europe multiple, different HASs have evolved in different countries or communities of collaborating countries, like Eurotransplant or Scandiatransplant. In this scientific statement, we review the generic ethical and practical principles underlying allocation principles and examine and describe different European HASs with the purpose of discussing impact of outcomes for patients with advanced HF. It is shown that European HASs differ significantly with respect to which patients are prioritized and the methods by which the prioritization is performed. It is argued that the most commonly used parameter to describe success of a HAS, namely 1-year survival after heart transplantation, is a poor metric of HAS performance. The impact of HASs should be evaluated by several measures such as survival from listing, time to transplantation, the characteristics of patients undergoing heart transplantation, and over a longer time interval to understand the balance of early and late post-transplant risks and benefit. Mapping European HASs is a step towards understanding these factors and further research should determine the optimal HAS in a given HF population at a given time.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".