Global patterns and trends in stomach cancer incidence: Age, period and birth cohort analysis
Bibliographic record
Abstract
The cases of stomach cancer (SC) incidence are increasing per year and the SC burden has remained very high in some countries. We aimed to evaluate the global geographical variation in SC incidence and temporal trends from 1978 to 2007, with an emphasis on the effect of birth cohort. Joinpoint regression and age-period-cohort model were applied. From 2003 to 2007, male rate were 1.5- to 3-fold higher than female in all countries. Rates were highest in Eastern Asian and South American countries. Except for Uganda, all countries showed favorable trends. Pronounced cohort-specific increases in risk for recent birth cohorts were seen in Brazil, Colombia, Iceland, New Zealand, Norway, Uganda and US white people for males and in Australia, Brazil, Colombia, Costa Rica, Czech Republic, Ecuador, Iceland, India, Malta, New Zealand, Norway, Switzerland, United Kingdom, Uganda, US black and white people for females. The cohort-specific ratio for male significantly decreased in Japan, Malta and Spain for cohorts born since 1950 and in Austria, China, Croatia, Ecuador, Russia, Switzerland and Thailand for cohorts born since 1960 and for female in Japan for cohorts born since 1950 and in Canada, China, Croatia, Latvia, Russia and Thailand for cohorts born since 1960. Disparities in incidence and carcinogenic risk persist worldwide. The favorable trends may be due to changes in environmental exposure and lifestyle, including decreased Helicobacter pylori prevalence, increased intake of fresh fruits and vegetables, the availability of refrigeration and decreased intake of salted and preserved food and smoking prevalence.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".