Clinical Utility of HCV Core Antigen Detection and Quantification in the Diagnosis and Management of Patients with Chronic Hepatitis C Receiving an All-Oral, Interferon-Free Regimen
Bibliographic record
Abstract
BACKGROUND: The introduction of highly potent direct-acting combination therapies for HCV have negated the role of response-guided therapy and reduced the role of treatment monitoring. However, there remains a need to identify patients who are actively infected with HCV and discriminate those who have achieved sustained virological response (SVR) from those who fail to achieve SVR. METHODS: TaqMan HCV RNA 2.0 assay. RESULTS: Using 10 fmol/l as the clinical cutoff for cAg, the HCV RNA and cAg tests were in 100% agreement for true negative samples and 99.6% agreement for truly positive samples. One discordant (screening) sample was identified. This sample was target not detected by HCV RNA method but positive by anti-HCV and highly positive by ARCHITECT core antigen (7,912 fmol/l). Seventeen samples had cAg levels in the 'grey zone' >3 but <10 fmol/l at initial testing and were re-tested per package insert. All of these samples gave a result of <3 fmol/l upon retest. These results were in alignment with target not detected HCV RNA result. One sample had a cAg >3 but <10 fmol/l when tested on three consecutive occasions (5.8, 5.5 and 4.4) but had a target not detected RNA result. CONCLUSIONS: In this study cAg, with a 10 fmol/l cutoff, accurately identified 99.6% of patients with active viraemia and discriminated all subjects who achieved SVR from those who failed therapy.
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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 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".