Estimating the impact of early hepatitis C virus clearance on hepatocellular carcinoma risk
Bibliographic record
Abstract
Although achieving sustained virological response (SVR) through antiviral therapy could reduce the risk of hepatocellular carcinoma (HCC) attributable to hepatitis C virus (HCV) infection, the impact of early viral clearance on HCC is not well defined. In this study, we compared the risk of HCC among individuals who spontaneously cleared HCV (SC), the referent population, with the risk in untreated chronic HCV (UCHC), those achieved SVR, and those who failed interferon-based treatment (TF). The BC Hepatitis Testers Cohort (BC-HTC) includes individuals tested for HCV between 1990-2013, integrated with medical visits, hospitalizations, cancers, prescription drugs and mortality data. This analysis included all HCV-positive patients with at least one valid HCV RNA by PCR on or after HCV diagnosis. Of 46 666 HCV-infected individuals, there were 12 527 (26.8%) SC; 24 794 (53.1%) UCHC; 5355 (11.5%) SVR and 3990 (8.5%) TF. HCC incidence was lowest (0.3/1000 person-years (PY)) in the SC group and highest in the TF group (7.7/1000 PY). In a multivariable model, compared to SC, TF had the highest HCC risk (hazard ratio (HR):14.52, 95% confidence interval (CI): 9.83-21.47), followed by UCHC (HR: 5.85; 95% CI: 4.07-8.41). Earlier treatment-based viral clearance similar to SC could decrease HCC incidence by 69.4% (95% CI: 57.5-78.0), 30% (95% CI: 10.8-45.1) and 77.5% (95% CI: 69.4-83.5) among UCHC, SVR and TF patients, respectively. In conclusion, using SC as a real-world comparator group, it showed that substantial reduction in HCC risk could be achieved with earlier treatment initiation. These analyses should be replicated in patients who have been treated with direct acting antiviral therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".