Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors
Bibliographic record
Abstract
Pancreatic cancer is the seventh leading cause of cancer-related deaths worldwide. However, its toll is higher in more developed countries. Reasons for vast differences in mortality rates of pancreatic cancer are not completely clear yet, but it may be due to lack of appropriate diagnosis, treatment and cataloging of cancer cases. Because patients seldom exhibit symptoms until an advanced stage of the disease, pancreatic cancer remains one of the most lethal malignant neoplasms that caused 432,242 new deaths in 2018 (GLOBOCAN 2018 estimates). Globally, 458,918 new cases of pancreatic cancer have been reported in 2018, and 355,317 new cases are estimated to occur until 2040. Despite advancements in the detection and management of pancreatic cancer, the 5-year survival rate still stands at 9% only. To date, the causes of pancreatic carcinoma are still insufficiently known, although certain risk factors have been identified, such as tobacco smoking, diabetes mellitus, obesity, dietary factors, alcohol abuse, age, ethnicity, family history and genetic factors, Helicobacter pylori infection , non-O blood group and chronic pancreatitis. In general population, screening of large groups is not considered useful to detect the disease at its early stage, although newer techniques and the screening of tightly targeted groups (especially of those with family history), are being evaluated. Primary prevention is considered of utmost importance. Up-to-date statistics on pancreatic cancer occurrence and outcome along with a better understanding of the etiology and identifying the causative risk factors are essential for the primary prevention of this disease. World J Oncol. 2019;10(1):10-27 doi: https://doi.org/10.14740/wjon1166
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".