Applicability of Hepatitis C Virus RNA Viral Load Thresholds for 8-Week Treatments in Patients With Chronic Hepatitis C Virus Genotype 1 Infection
Bibliographic record
Abstract
BACKGROUND: Interferon-free treatment of chronic hepatitis C virus (HCV) genotype 1 infection may be shortened to 8 weeks in treatment-naive, noncirrhotic patients with baseline HCV RNA levels of <4 or <6 million (M) IU/mL based on post-hoc analyses of phase 3 trial data. The applicability of these viral load thresholds in clinical practice is unknown. METHODS: Pretreatment and on-treatment serum samples (n = 740) from patients with HCV genotype 1 infection were included for HCV RNA analysis with 2 widely used assays, Cobas AmpliPrep/CobasTaqMan (CAP/CTM) and Abbott RealTime HCV (ART) assays. RESULTS: HCV RNA levels were significantly higher with CAP/CTM than with ART (overall difference, +0.11 log10 IU/mL; P < .001). In treatment-naive, noncirrhotic patients, discordance rates around the clinical cutoffs at 4M and 6M IU/mL were 23% and 18%, respectively. The mean differences between assays in discordant samples were 0.38 (4M) and 0.41 (6M) log10 IU/mL, respectively. Overall, 87% and 95% of treatment-naive, noncirrhotic patients, respectively, had baseline HCV RNA levels below 4M and 6M IU/mL with ART. These rates were significantly higher than those measured with CAP/CTM (64% and 78%, respectively; P < .001). Finally, discordance rates around the proposed thresholds in 2 consecutive samples of the same patient were in the range of 1%-2% for ART and 13%-17% for CAP/CTM. CONCLUSIONS: Selection of patients for 8-week regimens on the basis of a single HCV RNA determination may not be reliable because viral load levels around the proposed clinical thresholds show significant interassay and intrapatient variability.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".