Mortality and years of life lost of colorectal cancer in China, 2005–2020: findings from the national mortality surveillance system
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) is the fourth cause of cancer death in China. We aimed to provide national and subnational estimates and changes of CRC premature mortality burden during 2005-2020. METHODS: Data from multi-source on the basis of the national surveillance mortality system were used to estimate mortality and years of life lost (YLL) of CRC in the Chinese population during 2005-2020. Estimates were generated and compared for 31 provincial-level administrative divisions in China. RESULTS: Estimated CRC deaths increased from 111.41 thousand in 2005 to 178.02 thousand in 2020; age-standardized mortality rate decreased from 10.01 per 100,000 in 2005 to 9.68 per 100,000 in 2020. Substantial reduction in CRC premature mortality burden, as measured by age-standardized YLL rate, was observed with a reduction of 10.20% nationwide. Marked differences were observed in the geographical patterns of provincial units, and they appeared to be obvious in areas with higher economic development. Population aging was the dominant driver which contributed to the increase in CRC deaths, followed by population growth and age-specific mortality change. CONCLUSIONS: Substantial discrepancies were observed in the premature mortality burden of CRC across China. Targeted considerations were needed to promote a healthy lifestyle, expand cost-effective CRC early screening and diagnosis, and improve medical treatment to reduce CRC mortality among high-risk populations and regions with inadequate healthcare resources.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".