Hepatitis C virus core antigen: A simplified treatment monitoring tool, including for post-treatment relapse
Bibliographic record
Abstract
BACKGROUND: Simple, affordable diagnostic tools are essential to facilitate global hepatitis C virus (HCV) elimination efforts. OBJECTIVES: This study evaluated the clinical performance of core antigen (HCVcAg) assay from plasma samples to monitor HCV treatment efficacy and HCV viral recurrence. STUDY DESIGN: Plasma samples from a study of response-guided pegylated-interferon/ribavirin therapy for people who inject drugs with chronic HCV genotype 2/3 infection were assessed for HCV RNA (AmpliPrep/COBAS Taqman assay, Roche) and HCVcAg (ARCHITECT HCV Ag, Abbott Diagnostics) during and after therapy. The sensitivity and specificity of the HCVcAg assay was compared to the HCV RNA assay (gold standard). RESULTS: A total of 335 samples from 92 enrolled participants were assessed (mean 4 time-points per participant). At baseline, end of treatment response (ETR) and sustained virological response (SVR) visits, the sensitivity of the HCVcAg assay with quantifiable HCV RNA threshold was 94% (95% CI: 88%, 98%), 56% (21%, 86%) and 100%, respectively. The specificity was between 98 to 100% for all time-points assessed. HCVcAg accurately detected all six participants with viral recurrence, demonstrating 100% sensitivity and specificity. One participant with detectable (non-quantifiable) HCV RNA and non-reactive HCVcAg at SVR12 subsequently cleared HCV RNA at SVR24. CONCLUSIONS: HCVcAg demonstrated high sensitivity and specificity for detection of pre-treatment and post-treatment viraemia. This study indicates that confirmation of active HCV infection, including recurrent viraemia, by HCVcAg is possible. Reduced on-treatment sensitivity of HCVcAg may be a clinical advantage given the moves toward simplification of monitoring schedules.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".