Review article: the changing epidemiology of hepatocellular carcinoma in Canada
Bibliographic record
Abstract
The aim of this study was to examine the incidence of and mortality caused by hepatocellular carcinoma over the last 20 years in Canada, including the associated risk factors hepatitis C, diabetes and obesity. Databases from the Surveillance & Risk Assessment Division of Health Canada & Statistics Canada were analysed for trends in both age-adjusted incidence of and mortality due to hepatocellular carcinoma from 1984 to 2001. The epidemiological impact of hepatitis C, diabetes and obesity on hepatocellular carcinoma was also assessed. The incidence of hepatocellular carcinoma increased from 4.0 per 100,000 in 1984 to 5.5 in 2,000 for males, and from 1.6 per 100,000 in 1984 to 2.2 in 2,000 for females. Mortality rates showed a 48% increase in males and 39% increase in females. The incidence of hepatitis C increased sharply in 1995 and remained elevated until 2,000 with an average value of 85.4 per 100,000 in males and 45.4 per 100,000 in females. This increase is likely due to the widespread testing for hepatitis C. The prevalence of obesity and diabetes has increased in recent years and probably contributes to the increased incidence of hepatocellular carcinoma. The incidence of hepatocellular carcinoma in Canada has increased in the past 20 years and is associated with a rise in the incidence of hepatitis C, obesity and diabetes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".