HCV core antigen as an alternate test to HCV RNA for assessment of virologic responses to all-oral, interferon-free treatment in HCV genotype 1 infected patients
Bibliographic record
Abstract
In light of the advances in HCV therapy, simplification of diagnosis confirmation, pre- treatment diagnostic workup and treatment monitoring is required to ensure broad access to interferon-free therapies. HCV core antigen (HCV cAg) testing is rapid, giving results in approximately 60min, and less expensive than HCV RNA methods. While extensive data on the analytical performance of HCV cAg relative to RNA or comparisons in longitudinal studies of patients on interferon based (response guided) therapy there is very limited data on the relative performance of HCV cAg in diagnosis and monitoring patients receiving all-oral interferon free regimens. Furthermore, there is no data in the literature that describes the specificity of HCV cAg in patients with resolved HCV infection i.e. anti-HCV positive/HCV RNA negative. In this study a total of 1201 plasma samples from the 411 HCV genotype 1 subjects with a HCV RNA viral load >50,000IU/ml who enrolled in a clinical trial with ombitasvir, ritonavir-boosted paritaprevir and dasabuvir, with or without ribavirin were retrospectively tested in a blinded fashion with HCV cAg test and results were compared to HCV RNA levels. The specificity of the HCV cAg test was also evaluated in anti-HCV positive but HCV RNA negative samples. Overall concordance between HCV cAg and HCV RNA was 98.6% while concordance in pre-treatment samples was 99.5% (409/411; n=2 HCV RNA pos. with viral loads>3 Mill IU/ml but HCV cAg neg.) and 99.24% in post treatment week 12 samples (391/394; n=2 HCV RNA pos.<25IU/ml and n=1 HCV RNA pos. 2180IU/ml). Specificity in anti-HCV positive HCV RNA negative samples tested was 100%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".