Sirolimus‐based immunosuppression is associated with increased survival after liver transplantation for hepatocellular carcinoma†
Bibliographic record
Abstract
UNLABELLED: Liver transplantation is an important treatment option for selected patients with nonresectable hepatocellular carcinoma (HCC). Several reports have suggested a lower risk of posttransplant tumor recurrence with the use of sirolimus and a higher one with calcineurin inhibitors, but the selection of an ideal immunosuppression protocol is still a matter of debate. The aim of this study was to define the immunosuppression associated with the best survival after liver transplantation for HCC. It was based on the Scientific Registry of Transplant Recipients and included 2,491 adult recipients of isolated liver transplantation for HCC and 12,167 for non-HCC diagnoses between March 2002 and March 2009. All patients remained on stable maintenance immunosuppression protocols for at least 6 months posttransplant. In a multivariate analysis, only anti-CD25 antibody induction and sirolimus-based maintenance therapy were associated with improved survivals after transplantation for HCC (hazard ratio [HR] 0.64, 95% confidence interval [CI]: 0.45-0.9, P < or = 0.01; HR 0.53, 95% CI: 0.31-0.92, P < or = 0.05, respectively). The other studied drugs, including calcineurin inhibitors, did not demonstrate a significant impact. In an effort to understand whether the observed effects were due to a direct impact of the drug on tumor or more on liver transplant in general, we conducted a similar analysis on non-HCC patients. Although anti-CD25 induction was again associated with a trend toward improved survival, sirolimus showed a trend toward lower rates of survival in non-HCC recipients, confirming the specificity of its beneficial impact to cancer patients. CONCLUSION: According to these data, sirolimus-based immunosuppression has unique posttransplant effects on HCC patients that lead to improved survival.
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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 | 0.001 |
| 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) | 0.002 | 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".