Antiviral treatment for treatment-naïve chronic hepatitis B: systematic review and network meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Chronic hepatitis B (CHB) infection poses a significant burden to public health worldwide. Most cases are clinically silent until late in the disease course. The main goal of current therapy is to improve survival and quality of life by preventing disease progression to cirrhosis and liver failure, and consequently hepatocellular carcinoma development. The objective of this review is to provide a contemporary and comprehensive evaluation of the effectiveness of treatment options. METHODS: We performed a systematic review of peer-reviewed literature for randomized controlled trials involving treatment-naïve CHB adult population who received antiviral therapy. The endpoints were virologic response (VR), normalization of alanine aminotransferase (ALT norm), HBeAg loss, HBeAg seroconversion, and HBsAg loss for the HBeAg-positive population; and VR and ALT norm for the HBeAg-negative population. Network meta-analysis (NMA) was performed to synthesize evidence on the efficacy of treatment. RESULTS: Forty-two publications were selected. Twenty-three evaluated HBeAg-positive population, 13 evaluated HBeAg-negative population, and six evaluated both. We applied NMA to the efficacy outcomes of the two populations separately. Treatment strategies were ranked by the probability of achieving outcomes, and pairwise comparisons calculated from NMA were reported in odds ratios (OR). For HBeAg-positive population, tenofovir disoproxil fumarate (TDF) and tenofovir alafenamide (TAF) were the best for VR; OR vs adefovir = 14.29, 95% CI 7.69-25 and 12.5, 95% CI 4.35-33.33 respectively. TAF was the best for achieving ALT norm (OR vs placebo = 12.5, 95% CI 4.55-33.33), HBeAg loss, and seroconversion (OR vs entecavir/TDF combination = 3.03, 95% CI 1.04-8.84 and 3.33, 95% CI 1.16-10 respectively). In the HBeAg-negative population, TDF and TAF were the best for VR (OR vs adefovir = 9.79, 95% CI 2.38-42.7 and 11.71, 95% CI 1.03-150.48 respectively). Telbivudine and TAF were the best for ALT norm. Certain nucleos(t)ide combinations also had high probability of achieving positive outcomes. CONCLUSIONS: Our results are consonant with current clinical guidelines and other evidence reviews. For both HBeAg-positive and HBeAg-negative populations, TDF and TAF are the most effective agents for virologic suppression, and TAF is effective across all outcomes.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.028 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".