Time Trend of Upper Gastrointestinal Cancer Incidence in China from 1990 to 2019 and Analysis Using an Age–Period–Cohort Model
Bibliographic record
Abstract
The aim of this study was to investigate the upper gastrointestinal cancer incidence trend in China from 1990 to 2019 with Joinpoint software and to evaluate the age effect, cohort effect, and period effect using the age–period–cohort model, with the data obtained from the Global Burden of Disease, Injuries, and Risk Factors Study. The crude incidence rate (CR) of upper gastrointestinal cancer in China increased from 41.48/100,000 in 1990 to 62.64/100,000 in 2019, and the average annual percent change (AAPC) was 1.42 (p < 0.05). The age-standardized incidence rate (ASIR) decreased from 50.77/100,000 to 37.42/100,000, and the AAPC was −1.12 (p < 0.05). The net drift was −0.83 (p < 0.05), and the local drifts in the 35–79 age groups of males and all age groups of females were less than 0 (p < 0.05). The age effect showed that the upper gastrointestinal cancer onset risk gradually increased with age, the period effect was fundamentally manifested as a downward trend in onset risk after 2000, and the cohort effect indicated the decreased onset risk of the overall birth cohort after 1926. The ASIR of upper gastrointestinal cancer in China from 1990 to 2019 showed a downward trend, and the onset risk indicated the age, period, and cohort effects.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".