Hepatitis C RNA assay differences in results: Potential implications for shortened therapy and determination of Sustained Virologic Response
Bibliographic record
Abstract
Approval of Ledipasvir/Sofosbuvir for the treatment of chronic hepatitis C (HCV) includes the truncation of therapy from 12 to 8 weeks in treatment naïve, non-cirrhotic patients with baseline HCV RNA levels <6 million IU/mL (6.8 log10 IU/mL). The aim of this study was to evaluate this clinical cutoff with a different widely used commercially available HCV RNA test. Results from samples tested prospectively with Roche High Pure TaqMan HCV 2.0 test (HPS) were compared to those tested retrospectively with the Abbott RealTime HCV RNA test (ART). Using 6 million IU/mL as the cut-off, pre-treatment results were concordant in 70.4% of cases. When results with the same test measured at screening and baseline, clinical decisions could be impacted in 14.4% and 6.2% of cases for HPS and ART respectively. Using only HCV RNA cutoff of 6 million IU/mL, 29.55% of subjects would receive a different and potentially incorrect treatment duration based solely on HCV RNA test method used. A further 6-14% of subjects would have treatment decision change based on the day the sample was taken.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.119 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".