Predictive Factors of Lamivudine Treatment Success in a Hepatitis B Virus-Infected Pediatric Cohort: A 10-Year Study
Bibliographic record
Abstract
BACKGROUND: Hepatitis B virus (HBV) infections are responsible for the development of chronic hepatitis in 400 million people worldwide. Currently, no consensus exists as to when treatment should be initiated for pediatric patients. OBJECTIVES: To evaluate the risks and predictive factors of success of lamivudine treatment in children with chronic, active HBV infection. METHODS: Forty-three children (22 male, median age 9.6 years) chronically infected with HBV and treated between 1998 and 2008 at CHU Ste-Justine (Montreal, Quebec) were included in the present chart review study. Inclusion criteria were detectable hepatitis B surface antigen and hepatitis B e antigen (HBeAg), minimum serum alanine aminotransferase (ALT) level of two times the upper limit of normal and detectable serum HBV DNA for at least three months. Patients received lamivudine for a minimum of six months (median 14 months). Genotyping was performed. RESULTS: Lamivudine treatment was effective in 35% of cases (15 of 43) and overall virological response (during or after treatment) was achieved in 51% of patients. Three patients harboured suspected lamivudine-resistant mutations and five progressed to HBeAg-chronic HBV. Predictive factors for success of treatment were: younger age at beginning of treatment (P=0.05), elevated ALT levels throughout treatment duration (P=0.003) and loss of HBeAg during treatment (P=0.016). Asian origin did not affect treatment success or spontaneous viral control during follow-up. HBV genotype did not influence treatment success. CONCLUSIONS: Lamivudine treatment in a carefully selected cohort of HBV patients demonstrated a good rate of success and low incidence of mutation. Younger age at the beginning of treatment and high ALT levels during treatment predicted a positive outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".