Stomach Cancer Incidence and Mortality Trends among Circumpolar Nations
Bibliographic record
Abstract
BACKGROUND: Stomach cancer incidence and mortality rates are declining across circumpolar nations, but the burden may not be distributed equally across subpopulations, including Indigenous peoples. Our objective was to examine stomach cancer incidence and mortality trends across circumpolar populations. METHODS: Cancer incidence and mortality data from 1999-2016 were obtained from the Canadian Cancer Registry, Canadian Vital Statistics, CDC WONDER, NORDCAN, Northwestern Russian cancer registries, and National Cancer Reports. The direct method was used to calculate 10-year rolling age-standardized incidence and mortality rates to the world (WHO 2000-2025) and 2011 Canadian standard populations. Standardized incidence rate ratios (SRR) were calculated. Data were stratified by sex, year, and region. U.S. data were broken down by race [White; American Indian/Alaska Native (AIAN)]. Race data were not available from non-U.S. cancer registries. RESULTS: Most populations showed declining incidence and mortality rates over time. Incidence rates among Greenland males and females, Alaska AIAN males and females, and Northern Canadian both sexes were elevated compared with regional counterparts and remained stable. The largest male SRR was observed among Alaska AIAN versus Alaska Whites [SRR = 3.82; 95% confidence interval (95% CI), 2.71-5.37]. The largest female SRR was observed among Alaska AIAN versus Alaska Whites (SRR = 4.10; 95% CI, 2.62-6.43). CONCLUSIONS: Despite stomach cancer incidence and mortality rates declining overall, some northern and Indigenous populations experience elevated and stable incidence and mortality rates. IMPACT: There is a need to address disparities observed among circumpolar subpopulations. Given similarities in incidence, mortality, and risk factor prevalence across circumpolar regions, addressing disparities could benefit from coordinated international action.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".