Hepatocellular carcinoma incidence trends in Canada: analysis by birth cohort and period of diagnosis
Bibliographic record
Abstract
BACKGROUND AND AIMS: We examined birth cohort and calendar period trends in hepatocellular carcinoma (HCC) incidence in Canada (1976-2000). We also projected HCC incidence rates through 2015. PATIENTS AND METHODS: Data were obtained from the Canadian Cancer Registry on all cases of HCC diagnosed among persons aged 20 years and older in Canada from 1976 to 2000 and was used to describe trends in HCC incidence rates. RESULTS: We found that age-adjusted HCC incidence rates increased faster in males compared with females, 3.4% per year [95% confidence interval (CI): 3.0-3.8%] vs 2.2% per year (95% CI: 1.5-2.8%). An increasing birth cohort trend accelerated among males around the 1940 birth cohort and decelerated among females around the 1935 birth cohort. For calendar period trends, the increasing HCC risk was relatively constant over time among males whereas there was an acceleration in HCC risk around 1988 among females. Age-adjusted HCC incidence rates were projected to increase 73% in males and 28% in females from 1996 to 2015. CONCLUSIONS: Our results suggest that HCC incidence rates will continue to increase in Canada during the next decade as persons born in more recent birth cohorts, who face a relatively greater risk for HCC, age.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".