Donation after circulatory death in a European ultraperipheral and multi-insular region
Bibliographic record
Abstract
Abstract Background The creation of a new heart transplant (HT) program in a ultraperipheral region may be limited by a small population and a shortage of cardiac donors. In recent years, controlled donation after circulatory death (DCD) has significantly increased transplant activity. This study aims to evaluate the characteristics and outcomes of DCD using thoraco-abdominal normothermic regional perfusion (TANRP) and static cold storage compared to traditional donation after brain death (DBD) in the XXX HT program. Method We performed a single-center, retrospective observational study of all HT performed between December 2019 and December 2024 in the XXX hospital. Data were obtained from medical records and the XXX HT Registry. Results The first DCD transplant was performed in 2021, and in subsequent years 4 ,7 and 8 HT were procured using this technique (Figure 1A). During the study period, 98 HT were performed and 20% were from DCD. These donors had a similar age compared to DBD donors (49.8 vs. 46.4 years, p = 0.321), were mostly male (80%), and one patient had left ventricular dysfunction during the intraoperative DCD process. Mean waitlist time was shorter in the DCD group (27 vs 66 days, p = 0.03). There were no differences in primary graft dysfunction (10% in the DCD group and 9% in DBD, p = 1) or renal replacement therapy (50% vs 26%, p = 0.055). Median total hospital stay was longer in the DCD group (46 vs 36 days, p = 0.021). In the DCD group, at 1, 6, 12 and 24 months the survival rate was 96%, 95%, 93% and 87%, respectively, with no significant differences compared to the DBD group (Figure 1B). Conclusions In the first HT program in a European ultraperipheral and multi-insular region, DCD is performed safely and effectively without compromising outcomes, and with a significant increase in donor pool. This HT program employed older donors, than those reported in previous studies, with excellent short-term outcomes. Future studies are warranted.A: Number of DCD and DBD. B: Survival.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".