AB012. Distributions, risk factors and epidemiological trends of pancreatic cancer in low- and middle-income countries: a comprehensive analysis
Bibliographic record
Abstract
Background: Pancreatic cancer is an emerging public health concern in low- and middle-income countries (LMICs). Late diagnosis resulting from a lack of effective screening tools contributes to its poor prognosis and high mortality rate. The study investigates risk factors associated with pancreatic cancer to identify potential targets for prevention and intervention. Methods: We assessed the burden of pancreatic cancer in LMICs by analyzing sex differences, age groups, trends over time, risk factors, and disability-adjusted life years (DALYs) using publicly available global databases, such as the Global Burden of Disease (GBD) Study from the Institute for Health Metrics and Evaluation (IHME). Results: The DALYs peaks occurred at 70–74 for upper-middle income (816.33 DALYs); 70–79 for lower-middle income (496.96 and 495.55 DALYs, respectively); and 70–74 for low income (424.26 DALYs). Females experienced greater increases in DALYs than males across all income groups, with increases of (from high income to low income) 12.23%, 31.76%, 90.26%, and 63.62% between 1990 and 2019. The corresponding values for males were 3.19%, 39.95%, 62.05%, and 31.96%. Behavioral risks were the most prominent risk factor for pancreatic cancer, with DALYs decreasing by 13.45% in high-income countries and increasing by 29.23% to 32.81% in lower-income countries. Incidence has increased by 16.86% in high income countries, 45.32% in upper middle-income countries, 79.21% in lower middle income countries, and 48.51% in low income countries. Conclusions: In conclusion, the data provided highlights the significant burden of pancreatic cancer across all income categories, with the highest rates observed in older age groups and in high-income countries. The burden is further exacerbated by differences in rates between males and females across income categories. This study emphasizes the need for targeted prevention and early detection efforts, particularly in LMICs where resources and healthcare infrastructure may be limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".