Heart Transplantation With Donation After Circulatory Death
Bibliographic record
Abstract
Heart transplantation remains the preferred option for improving quality of life and survival for patients suffering from end-stage heart failure. Unfortunately, insufficient supply of cardiac grafts has become an obstacle. Increasing organ availability with donation after circulatory death (DCD) may be a promising option to overcome the organ shortage. Unlike conventional donation after brain death, DCD organs undergo a period of warm, global ischemia between circulatory arrest and graft procurement, which raises concerns for graft quality. Nonetheless, the potential of DCD heart transplantation is being reconsidered, after reports of more than 70 cases in Australia and the United Kingdom over the past 3 years. Ensuring optimal patient outcomes and generalized adoption of DCD in heart transplantation, however, requires further development of clinical protocols, which in turn require a better understanding of cardiac ischemia-reperfusion injury and the various possibilities to limit its adverse effects. Thus, we aim to provide an overview of the knowledge obtained with preclinical studies in animal models of DCD heart transplantation, to facilitate and promote the most effective and efficient advancement in preclinical research. A literature search of the PubMed database was performed to identify all relevant preclinical studies in DCD heart transplantation. Specific aspects relevant for DCD heart transplantation were analyzed, including animal models, graft procurement and storage conditions, cardioprotective approaches, and graft evaluation strategies. Several potential therapeutic strategies for optimizing graft quality are identified, and recommendations for further preclinical research are provided.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".