What Is Needed to Move Toward Single-Step Diagnosis of Current HCV Infection?
Bibliographic record
Abstract
Despite remarkable therapeutic advances, hepatitis C virus (HCV) infection continues to be a major global problem. While the development of highly effective direct-acting antivirals has ensured that almost all those who are treated achieve viral cure, progress toward HCV elimination globally has stalled due to challenges upstream of treatment in the cascade of care, namely diagnosis and linkage to care. The major challenge continues to be the relative complexity of HCV diagnosis with the current requirement for a confirmatory HCV RNA test after an initial antibody-positive result. In this review, challenges with the current paradigm are highlighted with a focus on new technologies, as well as simple strategies using existing tools, which may simplify diagnosis and improve linkage to care and treatment. To achieve HCV elimination, improvements in the HCV diagnostics field to allow for a simple single-step diagnosis are required.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".