Survival after diagnosis of hepatocellular carcinoma and potential impact of treatment in a hepatitis B or C infected cohort
Bibliographic record
Abstract
AIM: Little is known about the patterns of care and the impact of hepatocellular carcinoma (HCC) treatment on health outcomes at a population level. We conducted a population-based cohort study to examine HCC survival trends among people diagnosed with hepatitis B (HBV) or hepatitis C virus (HCV) infection, to determine predictors of receiving potentially curative therapy for HCC, and to examine the impact of HCC treatment on survival in New South Wales, Australia. METHODS: The Kaplan-Meier method was used to estimate survival, logistic regression to determine predictors of potentially curative therapy and Cox proportional hazards models to determine the impact of HCC treatment on survival. Years of potential life lost (YPLL) were calculated. RESULTS: During the period 1993-2007, 1081 cases of HCC were diagnosed. Median survival increased from 10.4 months during 1993-1997 to 18.4 months during 1998-2002, with no further improvement thereafter. Younger age at diagnosis (<65 years), being Asian-born and having multiple comorbid conditions increased the odds of receiving curative therapy. The effect of HCC treatment on the risk of mortality was similar between the HBV- and HCV-related HCC groups. Tumor-specific therapies had adjusted hazard ratios ranging 0.06-0.25 and palliative/supportive therapy alone had adjusted hazard ratios ranging 0.76-1.08. The average YPLL per person was 23.3. CONCLUSION: The burden of viral hepatitis-related HCC is substantial. Despite treatment advances in recent years, there has been no significant improvement in HCC survival. Efforts to improve HCC screening and early diagnosis are required to deliver curative treatment which clearly has a survival advantage.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".