Trends of gallbladder cancer incidence, mortality, and diagnostic approach in urban Shanghai between 1973 and 2009
Bibliographic record
Abstract
PURPOSE: To describe and interpret secular time trends in gallbladder cancer (GBC) incidence, mortality, and diagnostic approach using 37 years of cancer registry data in urban Shanghai. METHODS: Data on registration of GBC in urban Shanghai during 1973 and 2009 were collected by the Shanghai Cancer Registry. To describe time trends and to identify specific time points when significant changes occurred, we used joinpoint regression analysis. RESULTS: The age-standardized rates (ASRs) of incidence increased from 1.1/100,000 (1973-1975) to 2.9/100,000 (2006-2009) in men and from 1.7/100,000 (1973-1975) to 3.9/100,000 (2006-2009) in women. ASRs of incidence increased significantly with estimated annual percent changes (EAPCs) of 2.8% in men and 2.5% in women. The mortality trends increased significantly, with EAPCs of 2.8% in men and 2.5% in women. The increasing incidence and mortality rates were primarily observed in men ⩾60 years of age and in women ⩾70 years of age. Notable downward trends in incidence and mortality were identified among women age 60-69 years over the last decade. The percentage of GBC diagnosed by pathology increased steadily over the years while the percentage of GBC diagnosed by imaging, surgery, and biochemistry sharply increased from 1987 onwards. CONCLUSIONS: Thirty-seven years of cancer registry data document a tremendous increase in incidence/mortality and a slight decline in incidence/mortality over the last decades for GBC, especially among women, in Shanghai. The development of diagnostic approaches and aging population may play important roles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".