We have reached single-visit testing, diagnosis, and treatment for hepatitis C infection, now what?
Bibliographic record
Abstract
INTRODUCTION: Progress toward hepatitis C virus (HCV) elimination is impeded by low testing and treatment due to the current diagnostic pathway requiring multiple visits leading to loss to follow-up. Point-of-care testing technologies capable of detecting current HCV infection in one hour are a 'game-changer.' These tests enable diagnosis and treatment in a single visit, overcoming the barrier of multiple visits that frequently leads to loss to follow-up. Combining point-of-care HCV antibody and RNA tests should improve cost-effectiveness, patient/provider acceptability, and testing efficiency. However, implementing HCV point-of-care testing programs at scale requires multiple considerations. AREAS COVERED: This commentary explores the need for point-of-care HCV tests, diagnostic strategies to improve HCV testing, key considerations for implementing point-of-care HCV testing programs, and remaining challenges for point-of-care testing (including operator training, quality management, connectivity and reporting systems, regulatory approval processes, and the need for more efficient tests). EXPERT OPINION: It is exciting that single-visit testing, diagnosis, and treatment for HCV infection have been achieved. Innovations afforded through COVID-19 should facilitate the accelerated development of low-cost, rapid, and accurate tests to improve HCV testing. The next challenge will be to address barriers and facilitators for implementing point-of-care testing to deliver them at scale.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".