The long‐term efficacy of nucleos(t)ide analog plus a year of low‐dose<scp>HBIG</scp>to prevent<scp>HBV</scp>recurrence post‐liver transplantation
Bibliographic record
Abstract
Hepatitis B immunoglobulin (HBIG), given in combination with nucleos(t)ide therapy, has reduced the rate of recurrent hepatitis B virus (HBV) following liver transplantation (LT), although the most effective protocol is unknown. We have retrospectively evaluated the use of long-term nucleos(t)ide analog in combination with one yr of low-dose HBIG. One hundred and fifty-two adults with HBV-related liver disease underwent LT in our center from January 1999 to August 2009; of these, 132 patients who received one yr of HBIG combined with long-term nucleos(t)ide analogs (largely on lamivudine [LAM] alone, n = 97) afterward were included for the purposes of this study. Median follow-up post-transplantation was 1752 d. Patient survival was 93.9%, 86.9% and 84.1% at 1, 5, and 10 yr, respectively; none of the 17 deceased patients had recurrent HBV. HBV recurrence was observed in nine patients (all received LAM+HBIG), yielding recurrence rates of 2.3%, 5.1%, and 8.6% at 1, 3, and 5/10 yr, respectively. All recurrences were successfully managed, usually with additional antiviral treatment. In conclusion, this study, with its long-term follow-up, demonstrates that short course of low-dose HBIG (without anti-HBs monitoring) combined with the use of long-term nucleos(t)ide analog is effective and less cumbersome than many protocols in current use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 teacher head, 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".