Long-Term Effects of Everolimus-Facilitated Tacrolimus Reduction in Living-Donor Liver Transplant Recipients with Hepatocellular Carcinoma
Bibliographic record
Abstract
BACKGROUND The study objective was to evaluate the effect of everolimus (EVR) in combination with reduced tacrolimus (rTAC) compared with a standard TAC (sTAC) regimen on hepatocellular carcinoma (HCC) recurrence in de novo living-donor liver transplantation recipients (LDLTRs) with primary HCC at liver transplantation through 5 years after transplantation. MATERIAL AND METHODS In this multicenter, non-interventional study, LDLTRs with primary HCC, who were previously randomized to either everolimus plus reduced tacrolimus (EVR+rTAC) or standard tacrolimus (sTAC), and who completed the 2-year core H2307 study, were followed up. Data were collected retrospectively (end of core to the start of follow-up study), and prospectively (during the 3-year follow-up study). RESULTS Of 117 LDLTRs with HCC at LT in the core H2307 study (EVR+rTAC, N=56; sTAC, N=61), 86 patients (EVR+rTAC, N=41; sTAC, N=45) entered the follow-up study. Overall HCC recurrence was lower but statistically non-significant in the EVR+rTAC group (3.6% vs 11.5% in sTAC; P=0.136) at 5 years after LT. There was no graft loss or chronic rejection. Acute rejection and death were comparable between treatment groups. Higher mean estimated glomerular filtration rate in the EVR+rTAC group (76.8 vs 65.8 mL/min/1.73 m² in sTAC) was maintained up to 5 years. Reported adverse events were numerically lower in the EVR+rTAC group (41.0% vs 53.5% sTAC) but not statistically significant. CONCLUSIONS Although statistically not significant, early EVR initiation reduced HCC recurrence, with comparable efficacy and safety, and better long-term renal function, than that of sTAC treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".