Real-world Effectiveness of Sofosbuvir/Velpatasvir for Treatment of Chronic Hepatitis C in British Columbia, Canada: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Clinical trials show high efficacy of sofosbuvir/velpatasvir (SOF/VEL), but there are limited data from "real-world" settings. We aimed to evaluate SOF/VEL effectiveness for all hepatitis C virus (HCV) genotypes (GTs) in British Columbia (BC), Canada. METHODS: We used the BC Hepatitis Testers Cohort, which includes all HCV cases in the province (1990-2015) linked to administrative databases, including prescriptions to end of 2018. We measured sustained virologic response (SVR; negative RNA ≥10 weeks after treatment end) and identified characteristics associated with non-SVR. Conservatively, we excluded individuals with no assessment for SVR if their last RNA test after treatment initiation was negative (but included if positive). RESULTS: Of 2821 eligible participants, most were infected with GT1 (1076, 38.1%) or GT3 (1072, 38.0%), and a minority (278, 9.9%) were treated with RBV. SVR was 94.6% (2670/2821) overall and 94.5% (1017/1076) for GT1, 96.4% (512/531) for GT2, and 93.7% (1004/1072) for GT3. When disaggregated by GT, treatment regimen, and cirrhosis/treatment experience, SVR was lowest (30/40, 75.0%) among treatment-experienced GT3 individuals treated with RBV. Characteristics associated with non-SVR in multivariable analysis included younger age, RBV addition, and being a person with HIV (PWH) or who injects/injected drugs (PWID). When treatment regimen (±RBV) was removed from multivariable model, treatment experience was associated with non-SVR for GT3. Of 151 non-SVR individuals, 56.3% were nonvirological failures (treatment incomplete/no assessment for SVR) and 43.7% were virological failures (nonresponse/relapse). A disproportionately high percentage of non-SVR among PWID was due to nonvirological failure. CONCLUSIONS: SOF/VEL was highly effective in this "real-world" population-based cohort. Additional support is required for PWID/PWH to reach SVR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".