International patterns in incidence and mortality trends of pancreatic cancer in the last three decades: A joinpoint regression analysis
Bibliographic record
Abstract
BACKGROUND: Pancreatic cancer, as the one of most fatal malignancies, remains a critical issue in the global burden of disease. AIM: To estimate trends in pancreatic cancer incidence and mortality worldwide in the last three decades. METHODS: 100000) were presented. To estimate trends of incidence and mortality of pancreatic cancer, joinpoint regression analysis was used: the average annual percent change (AAPC) with the corresponding 95% confidence interval (95%CI) was calculated. Additionally, analysis was performed by sex and age. In this paper, the trend analysis included only countries with high and medium data quality. RESULTS: A total of 495773 (262865 male and 232908 female) new cases and 466003 (246840 male and 219163 female) deaths from pancreatic cancer were reported worldwide in 2020. In both sexes, most of the new cases (191348; 38.6% of the total) and deaths (182074; 39.1% of the total) occurred in the Western Pacific Region. In both sexes, the highest ASRs were found in the European Region, while the lowest rates were reported in the South-East Asia Region. The general pattern of rising pancreatic cancer incidence and mortality was seen across countries worldwide in observed period. Out of all countries with an increase in pancreatic cancer incidence, females in France and India showed the most marked rise in incidence rates (AAPC = +3.9% and AAPC = +3.7%, respectively). Decreasing incidence trends for pancreatic cancer were observed in some countries, but without significance. Out of all countries with an increase in pancreatic cancer mortality rates, Turkmenistan showed the most marked rise both in males (AAPC = +10.0%, 95%CI: 7.4-12.5) and females (AAPC = +6.4%, 95%CI: 3.5-9.5). The mortality trends of pancreatic cancer were decreasing in both sexes only in Canada and Mexico. CONCLUSION: Further research is needed to explain the cause of large international differences in incidence and mortality trends of pancreatic cancer in last three decades.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".