Liver transplantation in hepatitis B core–negative recipients using livers from hepatitis B core–positive donors: A 13-year experience
Bibliographic record
Abstract
The use of livers from hepatitis B surface antigen-negative (HBsAg- )/hepatitis B core antibody-positive (HBcAb+ ) donors in liver transplantation (LT) for HBsAg(-) /HBcAb- recipients is still controversial because of a lack of standard antiviral prophylaxis and long-term follow-up. We present our 13-year experience with the use of HBcAb+ donor livers in HBcAb- recipients. Patients received prophylaxis with hepatitis B immunoglobulin at the time of LT and then lamivudine daily. De novo hepatitis B virus (HBV) was defined as positive HBV DNA detection. Between January 1999 and December 2010, 1013 adult LT procedures were performed at our center. Sixty-four HBsAg- /HBcAb- patients (6.3%) received an HBsAg- /HBcAb+ liver. All donor sera were negative for HBcAb immunoglobulin M and HBV DNA. The mean follow-up was 48.8 ± 40.1 months (range = 1.2-148.8). Both the patient survival rates and the graft survival rates were 92.2% and 69.2% at 1 and 5 years, respectively. No graft losses or deaths were related to de novo HBV. Nine of the 64 patients (14.1%) developed de novo HBV. The mean time from LT to de novo HBV was 21.4 ± 26.1 months (range = 10.8-92.8 months). De novo HBV was successfully treated with adefovir or tenofovir. In conclusion, HBcAb+ allografts can be safely used in HBcAb- recipients without increased mortality or graft loss. Lifelong prophylaxis, continuous surveillance, and compliance are imperative for success. Should a de novo infection occur, our experience suggests that a variety of treatments can be employed to salvage the graft and obtain serum HBV DNA clearance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".