Early Allograft Dysfunction After Liver Transplantation With Donation After Circulatory Death and Brain Death Grafts: Does the Donor Type Matter?
Bibliographic record
Abstract
Background. Early allograft dysfunction (EAD) after liver transplantation has been associated with long-term reduced graft and patient survival. Methods. In this single-center cohort study, we aimed to compare incidence, risk factors, and outcomes in liver transplant recipients who developed EAD. Patients who received donation after circulatory death (DCD) or donation after brain death (DBD) grafts between January 2007 and December 2017 were included. EAD was defined as bilirubin of ≥10 mg/dL (171 μmol/L) or an international normalized ratio of ≥1.6 on postoperative day 7 or transaminases >2000 U\L in the first-week posttransplantation as previously described. Results. In our cohort of 1068 patients, incidence of EAD was 44%. EAD occurred more frequently in the DCD versus DBD group (71% versus 41%, P < 0.01). Overall, recipients who developed EAD showed a significantly lower graft and patient survival at 1, 3, and 5 y after transplantation (all P < 0.05). This was also the case for recipients of DBD grafts. However, for recipients of DCD grafts, patient and graft survival were not affected by the presence of EAD. For recipients of DBD grafts, donor age, body mass index (BMI) and gender, recipient BMI and model for end-stage liver disease score and warm and cold ischemia time were associated with EAD. For DCD recipients, donor BMI and cold ischemia time were associated with EAD. Conclusions. In our cohort study, EAD resulted in reduced long-term patient and graft survival only for DBD recipients but not for DCD recipients. Predictive markers for EAD were dependent on the donor type.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".