Bibliographic record
Abstract
Background and Objectives: Hepatitis B Virus (HBV) infection is one of the major public health challenges worldwide, affecting over 350 million people. The prevalence of this virus varies in different countries. Afghanistan, as a less developed country, has limited information about the epidemiological status of Hepatitis B. This study analyzes the prevalence and pattern of this disease in Afghanistan by reviewing existing sources to provide a better understanding of its situation and contribute to planning for Hepatitis B prevention and control in the country. Research Methodology: This study conducted a review of articles related to the prevalence and risk factors of Hepatitis B between 2010 and 2023, obtained from databases such as Scopus, PubMed, and Google Scholar. Data from 9 articles were extracted and the results were reported in a narrative synthesis following the PRISMA guidelines. Findings: This table provides a summary of the findings from 9 studies regarding the prevalence and risk factors of Hepatitis B in Afghanistan. According to these studies conducted between 2011 and 2022, the prevalence of Hepatitis B surface antigen (HBsAg) in the general population varied between 1.9% to 6.3%. Additionally, the prevalence in injecting drug users and prisoners was reported to be 3.7%-6.5% and 4.4%, respectively. Major risk factors included contaminated injections, risky sexual behavior, and homosexuality. Conclusion: Hidden HBV infection in HBsAg-negative individuals but HBV DNA positive poses a high risk for liver diseases and should be considered in epidemiological studies. Injecting drug users, sex workers, blood donors, and high-risk refugees require education and awareness regarding HBV prevention. Further studies are necessary to determine the precise prevalence of HBV, with a focus on prevention and control programs for injecting drug users and individuals engaging in risky sexual behaviors.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".