Therapeutic efficacy of hepatitis B virus vaccine in treatment of chronic HBV infections: A systematic review and meta‐analysis
Bibliographic record
Abstract
There is a need for improved treatment of patients with chronic hepatitis B (CHB). We reviewed the literature to explore the efficacy of HB vaccines alone or in combination therapy (CT) with antiviral drugs in CHB patients and to meta-analyze data from randomized controlled trials. We conducted a systematic search in ten databases. All studies investigating the efficacy of HBV vaccine in HBV infected patients were included with no restrictions. Among 1359 studies initially identified, 23 studies (n = 1956 patients) were included for the final analysis. CT showed a significant reduction of HBV DNA compared with analogue monotherapy (AM) at the 12-month follow-up period (odds ratio (OR) = 2.835, 95% confidence interval (CI) [1.275, 6.306], p = .011). Additionally, CT also remarkably induce HbsAg loss in comparison with AM (OR = 11.736, 95% CI [1.841, 74.794], p = .009). Our pooled data revealed no difference between treatment and control regarding alanine aminotransferase normalization, HBeAg seroconversion, and HBeAg disappearance. In addition, CT using vaccine and NAs resulted in a statistically significant higher incidence of adverse effects than AM. The therapeutic effects of combination therapy for patients with CHB were encouraging, but future studies need to investigate all possible treatment combinations and assess their cost-effectiveness.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".