Effect of sustained virologic response on liver-related mortality among individuals living with hepatitis C by treatment era: A population-based retrospective cohort study
Bibliographic record
Abstract
PURPOSE: Sustained virologic response (SVR) is a validated surrogate marker for successful hepatitis C virus (HCV) treatment. Historically, interferon-based therapies, the standard of care for decades, offered only limited efficacy with respect to SVR. The recent introduction of highly effective direct-acting antivirals (DAAs) revolutionised treatment, expanding treatment eligibility among individuals with advanced liver disease (ALD) and drug/alcohol-related substance use disorder. Given these clinical policy shifts, we assessed the real-world impact of SVR on liver-related death for these key clinical groups for whom treatment had previously been less feasible. METHODS: We conducted a population-based, cohort study of Ontario residents with HCV viremia between January 1st, 1999, and December 31st, 2018, with follow-up to May 31st, 2021 (N = 73,411) and used cause-specific hazard models to explore the association between SVR and liver-related death. RESULTS: SVR was associated with a significant reduction in liver-related deaths (adjusted hazard ratio [aHR]: 0.22, 95%CI: 0.20-0.24). This benefit was consistent across all levels of liver disease severity, including individuals with (aHR: 0.11, 95%CI: 0.06-0.18) and without (aHR: 0.13, 95%CI: 0.10-0.17) cirrhosis, individuals with ALD (aHR: 0.24, 95%CI: 0.22-0.27) as well as among individuals with (aHR: 0.24, 95%CI: 0.21-0.27) and without (aHR: 0.21, 95%CI: 0.18-0.24) substance use disorder. CONCLUSIONS: This study demonstrates the real-world impact of SVR on liver-related mortality and highlights the value of early treatment and continued support for populations who are marginalised.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".