Can we reduce ischemic cholangiopathy rates in donation after cardiac death liver transplantation after 10 years of practice? Canadian single-centre experience
Bibliographic record
Abstract
Background: Outcomes in liver transplantation with organs obtained via donation after cardiocirculatory death (DCD) have been suboptimal compared to donation after brain death, attributed mainly to the high incidence of ischemic cholangiopathy (IC). We evaluated the effect of a 10-year learning curve on IC rates among DCD liver graft recipients at a single centre. Methods: We analyzed all DCD liver transplantation procedures from July 2006 to July 2016. Patients were grouped into early (July 2006 to June 2011) and late (July 2011 to July 2016) eras. Those with less than 6 months of follow-up were excluded. Primary outcomes were IC incidence and IC-free survival rate. Results: Among the 73 DCD liver transplantation procedures performed, 70 recipients fulfilled the selection criteria, 32 in the early era and 38 in the late era. Biliary complications were diagnosed in 19 recipients (27%). Ischemic cholangiopathy was observed in 8 patients (25%) in the early era and 1 patient (3%) in the late era (p = 0.005). The IC-free survival rate was higher in the late era than the early era (98% v. 79%, p = 0.01). The warm ischemia time (27 v. 24 min, p = 0.049) and functional warm ischemia time (21 v. 17 min, p = 0.002) were significantly lower in the late era than the early era. Conclusion: We found a significant reduction in IC rates and improvement in IC-free survival among DCD liver transplantation recipients after a learning curve period that was marked by more judicious donor selection with shorter procurement times.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".