Comparing cohort and period trends of early-onset colorectal cancer: a global analysis
Bibliographic record
Abstract
BACKGROUND: Incidence of early-onset colorectal cancer (EOCRC) has increased globally in recent decades. We examined EOCRC incidence trends worldwide for potential cohort effects, defined as changes associated with time of birth (e.g., early-life exposure to carcinogens), and period effects, defined as changes associated with calendar periods (e.g., screening programs). METHODS: We obtained long-term incidence data for EOCRC diagnosed at age 20-49 through Year 2012 for 35 countries in the Cancer Incidence in Five Continents database. We used a smoothing method to help compare cohort and period trends of EOCRC, and used an age-period-cohort model to estimate cohort and period effects. RESULTS: Cohort effects had a more dominant role than period effects in the EOCRC incidence in Shanghai (China), the United Kingdom, Australia, New Zealand, Canada, the United States, and Osaka (Japan). The smoothed trends show the specific birth cohorts when EOCRC began to increase: the 1940s-1950s birth cohorts in the United States; the 1950s-1960s birth cohorts in other western countries; the 1960s birth cohorts in Osaka (Japan); and the 1970s-1980s birth cohorts in Shanghai (China). Such increases occurred earlier for early-onset cancers of the rectum than the colon. For the other countries, the results were less clear. CONCLUSIONS: Recent birth cohorts may have been exposed to risk factors different than earlier cohorts, contributing to increased EOCRC incidence in several developed countries or regions in the West and Asia. Such increases began in earlier birth cohorts in western countries than in developed regions of Asia.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".