Inferior survival in liver transplant recipients with hepatocellular carcinoma receiving donation after cardiac death liver allografts
Bibliographic record
Abstract
The impact of ischemia/reperfusion injury in the setting of transplantation for hepatocellular carcinoma (HCC) has not been thoroughly investigated. The present study examined data from the Scientific Registry of Transplant Recipients for all recipients of deceased donor liver transplants performed between January 1, 1995 and October 31, 2011. In a multivariate Cox analysis, significant predictors of patient survival included the following: HCC diagnosis (P < 0.01), donation after cardiac death (DCD) allograft (P < 0.001), hepatitis C virus-positive status (P < 0.01), recipient age (P < 0.01), donor age (P < 0.001), Model for End-Stage Liver Disease score (P < 0.001), recipient race, and an alpha-fetoprotein level > 400 ng/mL at the time of transplantation. In order to test whether the decreased survival seen for HCC recipients of DCD grafts was more than would be expected because of the inferior nature of DCD grafts and the diagnosis of HCC, a DCD allograft/HCC diagnosis interaction term was created to look for potentiation of effect. In a multivariate analysis adjusted for all other covariates, this interaction term was statistically significant (P = 0.049) and confirmed that there was potentiation of inferior survival with the use of DCD allografts in recipients with HCC. In conclusion, patient survival and graft survival were inferior for HCC recipients of DCD allografts versus recipients of donation after brain death allografts. This potentiation of effect of inferior survival remained even after adjustments for the inherent inferiority observed in DCD allografts as well as other known risk factors. It is hypothesized that this difference could reflect an increased rate of recurrence of HCC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".