Predictors of HBeAg loss after lamivudine treatment for chronic hepatitis B
Bibliographic record
Abstract
Elevated alanine transaminase (ALT) levels and low serum hepatitis B virus (HBV) DNA predict a higher likelihood of hepatitis B e antigen (HBeAg) loss in patients with chronic hepatitis B treated with interferon. Predictors of HBeAg loss in patients treated with lamivudine are not known. The objective of this analysis of 4 lamivudine-controlled Phase III trials was to determine patient-dependent or laboratory variables that predict HBeAg loss. Predictors of HBeAg loss in patients treated with interferon, lamivudine plus interferon, or placebo are also described. A total of 805 adults with chronic hepatitis B were treated either with lamivudine (n = 406), matching placebo (n = 196), interferon (n = 68), or the combination of lamivudine plus interferon (n = 135). Demographic and baseline disease characteristics were used in stepwise multivariate analyses to identify features that were predictive of lamivudine-induced HBeAg loss. HBeAg loss correlated with increased pretreatment ALT levels in all groups. The rate of HBeAg loss was highest among patients with pretreatment ALT levels greater than 5 times the upper limit of normal (ULN) and was most pronounced in the lamivudine group (56%). Multivariate modeling indicated that elevated baseline ALT levels (P <.001) and histologic activity index (HAI) score (P <.001) were important predictors of HBeAg loss in response to lamivudine. The effect of pretreatment ALT levels on HBeAg loss was similar for Asians and Caucasians. In conclusion, elevated pretreatment ALT levels and/or active histologic disease were the most important predictors of lamivudine-induced HBeAg loss. Asians and Caucasians had similar rates of response to lamivudine at comparable ALT levels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".