The impact of SVR from direct‐acting antiviral‐ and interferon‐based treatments for HCV on hepatocellular carcinoma risk
Bibliographic record
Abstract
We evaluated the effect of sustained virologic response (SVR) from direct-acting antiviral (DAA)- and interferon-based treatments on hepatocellular carcinoma (HCC) risk in a large population-based cohort in Canada. We used data from the BC Hepatitis Testers Cohort, which includes ~1.3 million individuals tested for HCV since 1990, linked with healthcare administrative and registry datasets. Patients were followed from the end of HCV treatment to HCC, death or 31 December 2016. We assessed HCC risk among those who did and did not achieve SVR by treatment type using proportional hazard models. Of 12 776 eligible individuals, 3905 received DAAs while 8871 received interferon-based treatments, followed for a median of 1.0 [range: 0.6-2.7] and 7.9 [range: 4.4-17.1] years, respectively. A total of 3613 and 6575 achieved SVR with DAAs- and interferon-based treatments, respectively. Among DAAs-treated patients, HCC incidence rate was 6.9 (95%CI: 4.7-10.1)/1000 person yr (PY) in SVR group (HCC cases: 26) and 38.2 (95%CI: 20.6-71.0) in the no-SVR group (HCC cases: 10, P < .001). Among interferon-treated individuals, HCC incidence rate was 1.8 (95%CI: 1.5-2.2) in the SVR (HCC cases: 99) and 13.9 (95%CI: 12.3-15.8) in the no-SVR group (HCC cases: 239, P < .001). Compared with no-SVR from interferon, SVR from DAA- and interferon-based treatments resulted in significant reduction in HCC risk (adjusted subdistribution hazard ratio (adjSHR) DAA = 0.30, 95%CI: 0.19-0.48 and adjSHR interferon = 0.2, 95%CI: 0.16-0.26). Among those with SVR, treatment with DAAs compared to interferon was not associated with HCC risk (adjSHR = 0.93, 95%CI: 0.51-1.71). In conclusion, similar to interferon era, DAA-related SVR is associated with 70% reduction in HCC risk.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".