Simplification of Care for Chronic Hepatitis C Virus Infection
Bibliographic record
Abstract
In 2016, the World Health Organization (WHO) set a target for eliminating viral hepatitis as a major public health threat by 2030. However, while today's highly effective and well-tolerated pangenotypic direct-acting antiviral regimens have maximized simplification of hepatitis C virus (HCV) treatment, there remain a plethora of barriers to HCV screening, diagnosis, and linkage to care. As of 2017, only 19% of the estimated 71 million individuals living with chronic HCV worldwide were diagnosed and in 2015 to 2016, only 21% of diagnosed individuals had accessed treatment. Simplification and decentralization of the HCV care cascade would bolster patient engagement and support the considerable scale-up needed to achieve WHO targets. Recent developments in HCV screening and diagnosis, together with reduced pretreatment assessment and on-treatment monitoring requirements, can further streamline the care continuum, ensuring patients are linked to care quickly and earlier in the disease course, and minimize clinic visits.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".