Trends in gastric cancer mortality 1990–2019 in 36 countries worldwide, with predictions to 2025, and incidence, overall and by subtype
Bibliographic record
Abstract
BACKGROUND: Gastric cancer (GC) incidence is declining heterogeneously worldwide. We aimed to calculate updated mortality trends for GC. METHODS: We investigated time trends for selected countries using the World Health Organization database. We computed age-standardized mortality rates (ASMR) per 100,000 persons over the 1990-2019 period. We reported rates for the 2010-2014 and 2015-19 calendar periods, and the corresponding percent changes. We used joinpoint regression analysis to identify changes in the slope of mortality trends, and predict the number of deaths and rates for 2025. We also reported 2008-2012 incidence rates of cardia and noncardia GC. RESULTS: Mortality trends from GC have been favorable since 1990 for all countries analyzed and the European Union (EU 27), in both sexes and all ages. GC mortality is predicted to decline in all countries for both sexes, except for French and US women aged 35-64 years, and Canadian men aged 35-64. The highest proportions of cardia GC were observed in Northern and Central Europe while the lowest ones in Southern and Eastern Europe. Elsewhere, the highest proportions were registered in countries with low incidence and mortality rates, whereas high-mortality countries showed lower proportions of cardia GC. CONCLUSION: Observed and predicted GC mortality trends declined in most countries in both sexes, with few exceptions, likely due to the control of GC risk factors, in particular Hp infection.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".