Increasing incidence in liver cancer in Canada, 1972-2006: Age-period-cohort analysis.
Bibliographic record
Abstract
BACKGROUND/AIMS: Our study aimed to assess 1) the temporal trends in incidence and mortality of liver cancer and 2) age-period-cohort effects on the incidence in Canada. METHODS: We analyzed data obtained from the Canadian Cancer Registry Database and Canadian Vital Statistics Death Database. We first examined temporal trends by sex, age group, and birth cohort between 1972 and 2006. Three-year period rates and annual percentage change (APC) were calculated to compare the changes over the study period. We used age-period-cohort modelling to estimate underlying effects on the observed trends in incidence. RESULTS: The overall age-adjusted incidence rates increased from 2.6 and 1.5 per 100 000 in 1972-74 to 6.5 (APC: 2.9) and 2.2 (APC: 1.2) per 100 000 in 2004-06 among males and females, respectively. The age-adjusted mortality rates increased from 3.3 and 2.0 per 100 000 in 1972-74 to 6.0 (APC: 2.3) and 2.6 (APC: 1.2) per 100 000 in 2004-06 among males and females, respectively. The incidence increased most rapidly in men aged 45-54 years (APC: 4.1) and women aged 65-74 years (APC: 1.7) over the period of study. CONCLUSIONS: The age-period-cohort analysis suggests that birth-cohort effect is underlying the increase in incidence. While the exact reason for the increased incidence of liver cancer remains unknown, reported increase in HBV and HCV infections, and immigration from high-risk regions of the world may be important factors.
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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 | 0.002 | 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 | 0.001 |
| Research integrity | 0.000 | 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".