Outcomes after liver transplantation using deceased after circulatory death donors: A comparison of outcomes in the UK and the US
Bibliographic record
Abstract
BACKGROUND AND AIMS: Identifying international differences in utilization and outcomes of liver transplantation (LT) after donation after circulatory death (DCD) donation provides a unique opportunity for benchmarking and population-level insight. METHODS: Adult (≥18 years) LT data between 2008 and 2018 from the UK and US were used to assess mortality and graft failure after DCD LT. We used time-dependent Cox-regression methods to estimate hazard ratios (HR) for risk-adjusted short-term (0-90 days) and longer-term (90 days-5 years) outcomes. RESULTS: One-thousand five-hundred-and-sixty LT receipts from the UK and 3426 from the US were included. Over the study period, the use of DCD livers increased from 15.7% to 23.9% in the UK compared to 5.1% to 7.6% in the US. In the UK, DCD donors were older (UK:51 vs. US:33 years) with longer cold ischaemia time (UK: 437 vs. US: 333 min). Recipients in the US had higher Model for End-stage Liver Disease (MELD) scores, higher body mass index, higher proportions of ascites, encephalopathy, diabetes and previous abdominal surgeries. No difference in the risk-adjusted short-term mortality or graft failure was observed between the countries. In the longer-term (90 days-5 years), the UK had lower mortality and graft failure (adj.mortality HR:UK: 0.63 (95% CI: 0.49-0.80); graft failure HR: UK: 0.72, 95% CI: 0.58-0.91). The cumulative incidence of retransplantation was higher in the UK (5 years: UK: 11.9% vs. 4.6%; p < .001). CONCLUSIONS: For those receiving a DCD LT, longer-term post-transplant outcomes in the UK are superior to the US, however, significant differences in recipient illness, graft quality and access to retransplantation were seen between the two countries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".