Effect of lamivudine treatment on survival of 309 North American patients awaiting liver transplantation for chronic hepatitis B
Bibliographic record
Abstract
The primary aim of this study is to determine whether treatment with lamivudine improved pre-liver transplantation (pre-LT) and LT-free survival of patients awaiting LT for hepatitis B virus (HBV)-related cirrhosis. Data from 162 lamivudine-treated and 147 untreated transplant candidates managed at 20 North American transplant centers between 1996 and 1998 were collected and compared. Lamivudine-treated patients were more likely to be men, hepatitis B e antigen positive, HBV DNA positive, and have lower serum albumin levels at listing (P <.05). Actuarial pre-LT and LT-free survival were similar in lamivudine-treated and untreated patients. Using Cox regression analysis, the only significant predictor of pre-LT patient survival was the modified Child-Turcotte-Pugh (mCTP) score, whereas significant predictors of LT-free survival included ethnic background, lamivudine treatment, indication for LT, baseline serum alanine aminotransferase level, and baseline mCTP score. Lamivudine had no apparent effect on liver disease severity in patients undergoing LT, but appeared to improve disease severity in patients still awaiting LT. Breakthrough infection was noted in 11% of lamivudine-treated patients. We conclude that lamivudine therapy is not associated with improved pre-LT or LT-free survival in LT candidates with chronic hepatitis B. However, a subset of patients with less advanced liver failure may derive clinical benefit from lamivudine therapy, thus delaying the need for LT. In the absence of prospective, randomized, controlled trials of lamivudine in patients with decompensated cirrhosis, careful selection of patients and optimal timing of treatment are needed to balance the risk versus benefit of lamivudine therapy in LT candidates.
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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 | 0.000 | 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".