Rapid Diagnostics for Hepatitis B and C Viruses in Low- and Middle-Income Countries
Bibliographic record
Abstract
The global health challenge posed by hepatitis B virus (HBV) and hepatitis C virus (HCV) persists, especially in low-and-middle-income countries (LMICs), where underdiagnosis of these viral infections remains a barrier to the elimination target of 2030. HBV and HCV infections are responsible for most liver-related mortality worldwide. Infected individuals are often unaware of their condition and as a result, continue to transmit these viruses. Although conventional diagnostic tests exist, in LMIC they are largely inaccessible due to high costs or a lack of trained personnel, resulting in poor linkage to care and increased infections. Timely and accurate diagnosis is needed to achieve elimination of hepatitis B and C by the year 2030 as set out by the World Health Organization Global Health Sector Strategy. In this review rapid diagnostic tests allowing for quick and cost-effective screening and diagnosis of HBV and HCV, are discussed, as are their features, including suitability, reliability, and applicability in LMIC, particularly those within Africa.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".