Global patterns of cardia and non-cardia gastric cancer incidence in 2012
Bibliographic record
Abstract
OBJECTIVE: Globally, gastric cancer incidence shows remarkable international variation and demonstrates distinct characteristics by the two major topographical subsites, cardia (CGC) and non-cardia (NCGC). Because global incidence estimates by subsite are lacking, we aimed to describe the worldwide incidence patterns of CGC and NCGC separately. DESIGN: Using Cancer Incidence in Five Continents Volume X (CI5X), we ascertained the proportions of CGC and NCGC by country, sex and age group (<65 and ≥65 years). These derived proportions were applied to GLOBOCAN 2012 data to estimate country-specific age-standardised CGC and NCGC incidence rates (ASR). Regional proportions were used to estimate rates for countries not included in CI5X. RESULTS: According to our estimates, in 2012, there were 260,000 cases of CGC (ASR 3.3 per 100,000) and 691,000 cases of NCGC (ASR 8.8) worldwide. The highest regional rates of both gastric cancer subsites were in Eastern/Southeastern Asia (in men, ASRs: 8.7 and 21.7 for CGC and NCGC, respectively). In most countries NCGC occurred more frequently than CGC with an average ratio of 2:1; however, in some populations where NCGC incidence rates were lower than the global average, CGC rates were similar or higher than NCGC rates. Men had higher rates than women for both subsites but particularly for CGC (male-to-female ratio 3:1). CONCLUSIONS: This study has, for the first time, quantified global incidence patterns of CGC and NCGC providing new insights into the global burden of these cancers. Country-specific estimates are provided; however, these should be interpreted with caution. This work will support future investigations across populations.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".