Epidemiology of cholangiocarcinoma
Bibliographic record
Abstract
Aim: We aimed to analyze temporal trends in mortality from intrahepatic (ICC) and extrahepatic (ECC) cholangiocarcinoma in selected countries worldwide. Methods: Official death certification data for ICC and ECC and populations estimates for 29 countries worldwide (17 from Europe, 8 from the Americas, and 4 from Australasia) and for Hong Kong Special Administrative Region of the People’s Republic of China (SAR), from 1995 to 2018, were extracted from the World Health Organization and the Pan American Health Organization databases. Age-standardized mortality rates were computed. A joinpoint regression analysis was performed. Results: In both sexes, ICC mortality rates increased in most countries considered, including the USA, the UK, and Australia; in some countries, including Italy and France, the increasing trends leveled off over the most recent years. In men, around 2016, the highest rates (1.7-2.3/100,000) were observed in Hong Kong SAR, Portugal, France, Spain, Australia, Austria, the UK, and Canada; Latin American countries and some eastern European countries had the lowest rates (0.2-0.8/100,000). A similar pattern was observed in women, but with lower rates (from 1.7/100,000 in Hong Kong SAR to 0.14/100,000 in Argentina). ECC mortality declined in most European and Australasian countries, but it tended to increase in Americas. In both sexes, rates were below 1/100,000 around 2016, with the only exceptions being Japan (2.6/100,000 men and 1.2/100,000 women) and Hungary (1.5/100,000 men and 1.1/100,000 women). Conclusion: ICC mortality increased in most areas of the world, likely due to increased prevalence of risk factors and improved cancer recognition and classification. ECC mortality fell in most countries, largely due to the widespread use of cholecystectomy.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".