Hepatitis B virus infection after immunization: How serious it is? An updated review
Bibliographic record
Abstract
Infection with hepatitis B virus (HBV) is one of the significant challenges worldwide. Despite the availability of antiviral drugs against this virus, the most critical strategy to prevent HBV infection is HB vaccination. Basically, despite widespread conventional HB vaccination, due to various reasons, including waning of hepatitis B surface antibody (HBsAb) titer after vaccination, the emergence of vaccine-escape mutants, failure to respond to the vaccine due to viral and host factors, levels of response in high-risk individuals and non-responders to conventional HB vaccination remains a major, unsolved and severe concern. This review focuses on the underlying reasons for conventional hepatitis B vaccination failures. It also suggests solutions to overcome these failures by highlighting significant advances in vaccination, including hepatitis B third-generation vaccines and adjuvanted hepatitis B vaccines as efficient alternatives to second-generation vaccines. Potentially, these new strategies will compensate for the shortcomings caused by second-generation vaccines. Adherence to these denouements has a significant role in preventing the circulation of HBV among individuals and reducing the global burden of HBV-related diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".