National time trends in mortality and graft survival following liver transplantation from circulatory death or brainstem death donors
Bibliographic record
Abstract
BACKGROUND: Despite high waiting list mortality rates, concern still exists on the appropriateness of using livers donated after circulatory death (DCD). We compared mortality and graft loss in recipients of livers donated after circulatory or brainstem death (DBD) across two successive time periods. METHODS: Observational multinational data from the United Kingdom and Ireland were partitioned into two time periods (2008-2011 and 2012-2016). Cox regression methods were used to estimate hazard ratios (HRs) comparing the impact of periods on post-transplant mortality and graft failure. RESULTS: A total of 1176 DCD recipients and 3749 DBD recipients were included. Three-year patient mortality rates decreased markedly from 19.6 per cent in time period 1 to 10.4 per cent in time period 2 (adjusted HR 0.43, 95 per cent c.i. 0.30 to 0.62; P < 0.001) for DCD recipients but only decreased from 12.8 to 11.3 per cent (adjusted HR 0.96, 95 per cent c.i. 0.78 to 1.19; P = 0.732) in DBD recipients (P for interaction = 0.001). No time period-specific improvements in 3-year graft failure were observed for DCD (adjusted HR 0.80, 95% c.i. 0.61 to 1.05; P = 0.116) or DBD recipients (adjusted HR 0.95, 95% c.i. 0.79 to 1.14; P = 0.607). A slight increase in retransplantation rates occurred between time period 1 and 2 in those who received a DCD liver (from 7.3 to 11.8 per cent; P = 0.042), but there was no change in those receiving a DBD liver (from 4.9 to 4.5 per cent; P = 0.365). In time period 2, no difference in mortality rates between those receiving a DCD liver and those receiving a DBD liver was observed (adjusted HR 0.78, 95% c.i. 0.56 to 1.09; P = 0.142). CONCLUSION: Mortality rates more than halved in recipients of a DCD liver over a decade and eventually compared similarly to mortality rates in recipients of a DBD liver. Regions with high waiting list mortality may mitigate this by use of DCD livers.
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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 | 0.003 |
| 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".