Risk of Hepatocellular Carcinoma After Direct-Acting Antiviral Treatment for Hepatitis C Virus Infection in People With HIV
Bibliographic record
Abstract
BACKGROUND: To inform hepatocellular carcinoma (HCC) surveillance after hepatitis C virus (HCV) cure with direct-acting antivirals (DAA), we estimated HCC risk post-DAA in people with human immunodeficiency virus (HIV) with advanced liver fibrosis or cirrhosis under universal DAA. METHODS: We used data from HepCAUSAL, a collaboration of cohorts of HIV-HCV coinfection from Europe and North America. Eligibility criteria were HIV-HCV coinfection, advanced liver fibrosis or cirrhosis, DAA naïve, HIV-RNA < 50 copies/mL, on antiretroviral therapy, no HBV coinfection, and no prior HCC diagnosis or liver transplant. Follow-up started when eligibility was met and ended at HCC diagnosis, death, loss to follow-up, 6 years, or database closure, whichever came first. We estimated the 6-year risk and annual probability of HCC if all eligible individuals had initiated DAA at baseline using a weighted pooled logistic model for the monthly HCC risk among DAA initiators. RESULTS: Of 3824 eligible individuals (92% males, median age 60 years [IQR: 54, 64]), 2373 (62%) who initiated DAA, 43 had an HCC diagnosis during follow-up. The estimated 6-year HCC risk (95% CI) under universal DAA was 2.5% (1.6, 3.9). Annual HCC probability was 0.81% (0.34, 1.54) between baseline and month 12 after DAA initiation, 0.64% (0.28, 1.19) between years 1 and 2, 0.50% (0.27, 0.84) between years 2 and 3, 0.34% (0.13, 0.63) between years 3 and 4, 0.19% (0.07, 0.33) between years 4 and 5, and 0.10% (0.01, 0.24) between years 5 and 6. CONCLUSIONS: An estimated 2.5% of people with HIV and advanced liver fibrosis or cirrhosis are diagnosed with HCC by 6 years post-DAA. Annual probability of HCC declines over time and falls below 0.4% after 3 years.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".