Treatment Status of Hepatocellular Carcinoma Does Not Influence Rates of Sustained Virologic Response: An HCV‐TARGET Analysis
Bibliographic record
Abstract
Recent studies have suggested a negative impact of hepatocellular carcinoma (HCC) on sustained virologic response (SVR) to hepatitis C virus (HCV) direct acting antivirals (DAAs). We compared the effectiveness of DAAs in patients with cirrhosis, with and without HCC, and in those with HCC partially treated or untreated (PT/UT‐HCC) versus completely treated (CT‐HCC). HCC status was based on imaging 6 months before or 2 months after start of DAA therapy. Absence and presence of enhancing lesions after HCC treatment defined CT‐HCC and PT/UT‐HCC, respectively. Using minimally adjusted logistic regression, the association between the presence of HCC and SVR rates was estimated. Among the 1,457 patients with cirrhosis from HCV‐TARGET with complete virologic data (per‐protocol population) who did not undergo liver transplantation during treatment and followup, 1,300 were without HCC, 91 with CT‐HCC, and 66 with PT/UT‐HCC. Most patients were genotype 1 (81%) and treatment‐experienced (56%), 41% had history of prior decompensation, and the median pretreatment Model for End‐Stage Liver Disease was 9 (range 6‐39). The SVR rates were 91% for patients without HCC, 84% for CT‐HCC, and 80% for PT/UT‐HCC. The presence of HCC (versus not having HCC) was associated with significantly lower odds of achieving SVR (odds ratio [OR] = 0.51, 95% confidence interval [CI]: 0.33‐0.81; P = 0.003). However, among those with HCC, HCC treatment status (PT/UT‐HCC versus CT‐HCC) did not show association with SVR (OR = 0.79, 95% CI: 0.35‐1.79, P = 0.569). Conclusions: The presence of HCC reduces the likelihood of SVR by 50%, but with no evident difference in those with completely treated HCC versus partially treated/untreated HCC.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".