Bibliographic record
Abstract
In order to compare the age-specific cancer incidence rate between Japan and other countries, we abstracted cancer incidence rate from the Cancer Incidence in Five Continents Vol. XI (CI5) (1). The International Agency for Research on Cancer provides the CI5 databases on the incidence of cancer recorded by cancer registries (regional and national) worldwide. We used cancer incidence rate in five countries in Asia (China, India, Japan, the Republic of Korea and Thailand), three countries in America (the USA, Canada and Brazil), two countries in Oceania (Australia and New Zealand) and four countries in Europe (the UK, France and Germany and Italy). Some countries have plural cancer registries, and we aggregated all registries to calculate the incidence rate in the countries from the CI5-XI database. The year of cancer diagnosis was from 2008 to 2012. All cancer were coded as C00-97. Figure 1 shows age-specific cancer incidence rate in male by countries, studied. In Asia except for Japan and Korea, the overall incidence rate was lower than the other two areas. In addition, variance of incidence among the studied countries was larger in Asia than those in America and Oceania and Europe. The shape of curve seemed linear in America and Oceania and had “S” shape in Asia. In Europe, a slowdown of cancer incidence increase was observed around the age of 35. Childhood cancer incidence rates were similar in Asia and in America and Oceania; however, the incidence in the age group 5–14 was higher in Europe compared with the other two areas. The incidence in the AYA generation was also higher in Europe. Cancer incidence in the younger age groups (−40) is lower in Asia except Korea; it reached at 100/100 000 around the age of 40; however, in America and Oceania, and Europe, incidence exceeded 100/100 000 in their 30s. Incidence increased then rapidly in Japan until the age of 55. In America and Oceania, increase was constant until the age of 65 and slowed down until the highest age group. The shape of incidence was similar to that in Asia; increase was steep until the age of 65.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".