Impact of an Open Access Nationwide Treatment Model on Hepatitis C Virus Antiviral Drug Resistance
Bibliographic record
Abstract
Direct acting antivirals (DAAs) have revolutionized hepatitis C virus (HCV) treatment, but drug resistance could undermine proposed global elimination targets. Real‐world studies are needed to inform the impact of widespread DAA treatment on antiviral resistance in the community. The prevalence and range of posttreatment resistance‐associated substitutions (RASs) was determined in Australian patients with open access to DAAs through a wide range of prescribers. NS3, NS5A, and NS5B regions were amplified by polymerase chain reaction and analyzed by population sequencing. Clinically relevant RASs were identified using online databases (ReCALL and Geno2Pheno[hcv]). Of 572 samples, 60% were from genotype 3 and 27% from genotype 1a. Ninety‐two percent of people failed a DAA regimen containing an NS5A inhibitor, including 10% with a pangenotype regimen. NS5A RASs were detected in 72% of people with genotype 1 and 80% with genotype 3. For genotype 1, there was a range of RASs across the NS5A region, while for genotype 3, the Y93H RAS predominated (72%). The prevalence of NS3 RASs was higher in people exposed to an NS3 inhibitor (35% vs. 3.9%; P < 0.0001). NS5B resistance was rare, with a single case of sofosbuvir resistance. Multiclass drug resistance was found in 33% of people exposed to both NS3 and NS5A inhibitors. Conclusion: The high prevalence of NS5A RASs among people failing DAA therapy reinforces the importance of specific retreatment regimens, ideally guided by resistance testing. The impact of multiclass drug resistance on retreatment in people exposed to both NS3 and NS5A inhibitors needs to be assessed in real‐world studies. Surveillance for increasing antiviral resistance during treatment scale‐up is essential to maintain the efficacy of current DAA regimens.
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".