A scoping review on HCV screening strategies: population to screen and the test types
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) is a genetically diverse blood-borne pathogen causing liver inflammation and damage. It is one of the global public health problems responsible for claiming thousands of lives every year. Although there are various HCV testing strategies depending on the specific circumstances and guidance of local authorities, the proportion of diagnosed HCV cases in Low- and Middle-Income Countries (LMICs) is estimated to be less than 5%. This review analyzes and documents evidence for different ways of screening HCV. METHODS: The updated Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines have been used as the basis for this scoping review. Retrieved articles were screened and extracted by three independent individuals to make sure that all pertinent literatures were included. RESULTS: A total of 8318 records were retrieved from four electronic databases (Scopus, PubMed, CINAHL and Google Scholar). Of the total retrieved records and after applying the pre-defined inclusion criteria, we included 51 studies in this review. According to the studies included in this review, three major screening approaches were noted: The universal, targeted, and risk-based HCV screening strategies. Population to screen include the baby boomer cohort, pregnant women, key populations, those experiencing homelessness, adults visiting health facilities, employees, and social event attendants. A "one-stop-shop" HCV testing initiative at different settings, such as prisons, addiction rehabilitation centers, and community dropping centers, were found to increase HCV test uptake among key populations. Integrating HCV screening with the existing HIV and Sexually Transmitted Infection (STI) clinics was highlighted to identifying and linking HCV-infected individuals to appropriate care and treatment. Although there are many ways of diagnosing HCV for treatment, identifying those who were reactive for HCV antibody first, followed by an HCV-antigen test for those antibody-positive, were found to be the most cost-efficient way of diagnosing HCV infection. CONCLUSION: HCV screening among pregnant women, the baby boomer cohort, adults visiting health facilities, engaging in injection drug use, incarcerated individuals, and those experiencing homelessness are useful approaches in identifying HCV-antibody positive individuals. An efficient way to reach the most at-risk people is to incorporate HCV screening into community service centers and clinics.
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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 | 0.020 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".