International trends in anal cancer incidence rates
Bibliographic record
Abstract
Background: Previous studies have reported rapid increases in anal cancer incidence rates in seven high-income countries in North America, Europe and Oceania. There is very limited information on whether this pattern is replicated in other parts of the world. In this study, we examine recent trends in anal cancer incidence in 18 countries worldwide. Methods: We calculated age-standardized incidence rates for anal squamous cell carcinoma (ASCC) and anal adenocarcinoma (AAC) for a minimum of 13 years through to 2007, using data from the International Agency for Research on Cancer's Cancer Incidence in Five Continents series, and applied joinpoint regression models to assess changes in incidence rates. We also conducted an extended analysis of the data from the USA through to 2012. Results: ASCC was the main histological subtype in most of the countries considered in this analysis. The incidence of ASCC increased in both men and women in several high-income countries, including Australia, Canada, Denmark, France, Italy, Netherlands, the UK and the USA, whereas it increased only in women in Colombia, Estonia, the Russian Federation, Slovakia and Switzerland. Conversely, there was little change in the incidence of ASCC in either men or women in India, Israel, Japan, Singapore and Spain. The incidence rates of AAC decreased or were stable in most populations. Conclusions: The ASCC incidence rates increased in both men and women or in women in all countries included in this study, except Asian countries and Spain, where the rates remained unchanged. Population-based preventive measures, including human papillomavirus vaccination and advocacy for safe sexual behaviours, may contribute to curbing the surging burden of the disease.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".