Global Patterns and Trends in Pancreatic Cancer Incidence
Bibliographic record
Abstract
OBJECTIVES: We aim to provide a global geographical picture of pancreatic cancer incidence and temporal trends from 1973 to 2015 for 41 countries. METHODS: Joinpoint regression and age-period-cohort model was used. RESULTS: In 2012, the highest age-adjusted rate was in Central and Eastern Europe for males and North America for females. Most regions showed sex disparities. During the recent 10 years, increasing trends were observed in North America, Western Europe, and Oceania. The greatest increase occurred in France. For recent birth cohorts, cohort-specific increases in risk were pronounced in Australia, Austria, Brazil, Canada, Costa Rica, Denmark, Estonia, France, Israel, Latvia, Norway, Philippines, Republic of Korea, Singapore, Spain, Sweden, the Netherlands, United States, and US white male populations and in Australia, Austria, Brazil, Bulgaria, Canada, China, Czech Republic, Finland, France, Italy, Japan, Lithuania, Norway, Republic of Korea, Singapore, Spain, The Netherlands, United Kingdom, United States, and US white female populations. CONCLUSIONS: In contrast to the favorable effect of the decrease in smoking prevalence, other factors, including the increased prevalence of obesity and diabetes and increased physical inactivity, increased intake of red or processed meat and inadequate intake of fruits and vegetables are likely to have an unfavorable role in pancreatic cancer incidence worldwide.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".