Distribution, Risk Factors and Epidemiological Trends of Pancreatic Cancer Across Countries’ Income Levels: A Comprehensive Analysis
Bibliographic record
Abstract
BACKGROUND: Globally, pancreatic cancer poses a significant concern for public health. AIMS: The objective of this study was to assess the burden of pancreatic cancer on varying income levels. METHODS AND RESULTS: Data from the Global Burden of Disease Study (GBD) 2021 and Gross Domestic Product Per Capita data were utilised in this study. All countries were categorised into four groups based on their income levels. Age-standardised incidence, mortality and disability-adjusted life years (DALYs) rates were the primary parameters to analyse the burden of pancreatic cancer. The associations between pancreatic cancer burden and countries' economic levels were analysed with linear regression models. High-income-level countries generally had a higher burden compared to other income levels in 2021. Greenland had the highest rate of age-standardised DALYs at 374.93 per 100 000, followed by Uruguay (297.06) and Monaco (290.87). A higher gross domestic product (GDP) per capita was linked to a higher age-standardised incidence (β = 0.77, 95% CI = 0.63, 0.90, p < 0.001), mortality (β = 0.72, 95% CI = 0.59, 0.86, p < 0.001) and DALYs (β = 14.59, 95% CI = 11.38, 17.80, p < 0.001). From 1990 to 2021, the pancreatic cancer burden increased across all income levels, with the most pronounced rise seen in lower-middle-income countries. Smoking-related age-standardised DALYs have decreased since 1990. However, there was a notable increase in males in upper-middle-income countries during the same period. CONCLUSION: In conclusion, the pancreatic cancer burden has been increasing globally. The burden of pancreatic cancer varies significantly among countries with different income levels. Effective preventions are needed to control the burden of pancreatic cancer.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".