Trends in incidence of hepatocellular carcinoma after diagnosis of hepatitis B or C infection: a population‐based cohort study, 1992–2007
Bibliographic record
Abstract
Chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections are the major risk factors for hepatocellular carcinoma (HCC). We examined trends in the incidence of HCC among a population-based cohort of people infected with HBV or HCV. HBV and HCV cases notified to the New South Wales Health Department between 1992 and 2007 were linked to the Central Cancer Registry, Registry of Births, Deaths and Marriages, and National HIV/AIDS Registries. Crude HCC incidence rates were estimated using person-time methodology. Age-standardized incidence rates were calculated using the 2001 Australian population. Trends in incidence were examined using join point regression models. Between 1992 and 2007, 1201 people had a linked HCC record: 556 of those with HBV; 592 with HCV; 45 with HBV/HCV co-infection; and 8 with HIV co-infection. The overall age-standardized HCC incidence rates declined non-significantly from 148.0 (95% confidence intervals (CI) 63.7, 287.4) per 100,000 population in 1995 to 101.2 (95% CI 67.3, 144.6) in 2007 among the HBV monoinfected group and significantly from 151.8 (95% CI 62.4, 299.8) per 100,000 population to 75.3 (95% CI 50.8, 105.5) among the HCV monoinfected group. However, incidence rates in the HCV monoinfected group progressively increased from the period 1992-1997 to 2004-2007 when adjusted for age, sex, and birth cohort, and the total number of cases per annum continued to increase. Despite declines in the age-adjusted incidence rates of HCC over time, the absolute number of cases increased likely due to the ageing cohort and an increasing prevalence of both hepatitis B and C in Australia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".