Colorectal cancer burden and trends: Comparison between China and major burden countries in the world
Bibliographic record
Abstract
OBJECTIVE: To summarize the colorectal cancer (CRC) burden and trend in the world, and compare the difference of CRC burden between other countries and China. METHODS: Incidence and mortality data were extracted from the GLOBOCAN2018 and Cancer Incidence in Five Continents. Age-specific incidence trend was conducted by Joinpoint analysis and average annual percent changes were calculated. RESULTS: About 1.85 million new cases and 0.88 million deaths were expected in 2018 worldwide, including 0.52 million (28.20%) new cases and 0.25 million (28.11%) deaths in China. Hungary had the highest age-standardized incidence and mortality rates in the world, while for China, the incidence and mortality rates were only half of that. CRC incidence and mortality were highly correlated with human development index (HDI). Unlike the rapid increase in Republic of Korea and the downward trend in Canada and Australia, the age-standardized incidence rates by world standard population in China and Norway were rising gradually. The age-specific incidence rate in the age group of 50-59 years in China was increasing rapidly, while in Republic of Korea and Canada, the fastest growing age group was 30-39 years. CONCLUSIONS: The variations of CRC burden reflect the difference of risk factors, as well as levels of HDI and screening (early detection activities). The burden of CRC in China is high, and the incidence of CRC continues to increase, which may lead to a sustained increase in the burden of CRC in China in the future. Screening should be expanded to control CRC, and focused on young people in China.
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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 | 0.004 | 0.005 |
| 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".