Declining incidence of hepatitis C related hepatocellular carcinoma in the era of interferon‐free therapies: A population‐based cohort study
Bibliographic record
Abstract
BACKGROUND & AIMS: The impact of interferon (IFN)-free therapies on the epidemiology of hepatitis C virus (HCV) related hepatocellular carcinoma (HCC) is not well understood at a population level. Our goal was to bridge this evidence gap. METHODS: This study included all patients in Scotland with chronic HCV and a diagnosis of cirrhosis during 1999-2019. Incident cases of HCC, episodes of curative HCC therapy, and HCC-related deaths were identified through linkage to nationwide registries. Three time periods were examined: 1999-2010 (pegylated interferon-ribavirin [PIR]); 2011-2013 (First-generation DAA); and 2014-2019 (IFN-free era). We used regression modelling to determine time trends for (i) number diagnosed and living with HCV cirrhosis, (ii) HCC cumulative incidence, (iii) HCC curative treatment uptake and (iv) post-HCC mortality. RESULTS: 3347 cirrhosis patients were identified of which 381 (11.4%) developed HCC. After HCC diagnosis, 140 (36.7%) received curative HCC treatment and there were 202 deaths from HCC. The average annual number of patients diagnosed and living with HCV cirrhosis was approximately seven times higher in the IFN-free versus the PIR era, whereas the number of incident HCCs was four times higher. However, the cumulative incidence of HCC was significantly lower in the IFN-free versus PIR era (sdHR: 0.65; 95%CI:0.47-0.88; P = .006). Among HCC patients, diagnosis in the IFN-free era was not associated with improved uptake of curative treatment (aOR:1.18; 95%CI:0.69-2.01; P = .54), or reduced post-HCC mortality (sdHR: 0.74; 95%CI:0.53-1.05; P = .09). CONCLUSIONS: The cumulative incidence of HCC is declining in HCV cirrhosis patients, but uptake of curative HCC therapy and post-HCC survival remains suboptimal.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".