Rapid Diagnostics for Hepatitis B and C Viruses in Low- and Middle-Income Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it