Bibliographic record
Abstract
In order to make a comparison of the age-specific incidence rate of leukaemia 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, 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, Germany and Italy). Some countries have plural cancer registries and we aggregated all the registries to calculate the incidence rate in the countries from the CI5-XI database. The period of years at cancer diagnosis was from 2008 to 2012. In this study, leukaemia included lymphoid leukaemia coded as C91, Myeloid leukaemia coded as C92-C94 and leukaemia unspecified coded as C95 based on ICD-10. Figure 1 shows the age-specific incidence rate of leukaemia in male by 5-year age groups for the selected countries. Age-specific incidence rate of leukaemia per 100,000 people in male. The pattern of the age-specific incidence curves was similar in the most countries, studied. The incidence rates decreased until 20–30 years of age, then increased with age. There is little difference between areas and countries up to about age 30, but it becomes greater at later age. In general, the incident rates were higher in America and Oceania (except Brazil) and European countries than those in Asia and Brazil, especially among over 45 years of age. In Asia, the incidence rates were highest in Japan, lowest in India and similar between the other three countries (China, the Republic of Korea and Thailand). The incidence rates for the oldest age group (85 years and older, 75 years and older only in India) were 48 (per 100 000 population) in Japan and 16 in India, and about 30 in the other three countries. Age-specific incidence rate of leukaemia per 100,000 people in female. The incidence rates for countries in America (except Brazil) and Oceania were similar with little difference. The rates in Brazil were similar to that of other American countries for those under 30 years of age, but the difference increases for those over 30 years of age. In Europe, the difference in incidence rate among four countries was relatively small. The UK had the highest incidence rate at 122 (per 100 000 population aged 85 and over), followed by France at 116, Germany at 106 and Italy at 97. Figure 2 shows the age-specific incidence rates of leukaemia in female. In most countries, the incidence rate for female was lower than for male at almost all ages, and the gender difference increased with age, especially after 45 years of age. In the age group, which has the highest incidence (75–79 or 80–84 years of age in many countries), the incidence rate of females was about half those of males. The trends of high or low incidence rates by age group in the countries of the Americas, Oceania and Europe were similar in male and female but were slightly different in Asia. For women, the Republic of Korea, but not Japan, had the highest incidence rates among those under 70 years of age. However, the incidence rates in the Republic of Korea peak in the 75–79 age group and then rapidly decreased, and in the 85 and older age group, the incidence rate is about the same as those in countries other than Japan in Asia. Note: Data were downloaded from the Global Cancer Observatory, which is an interactive web-based platform presenting global cancer statistics (https://gco.iarc.fr/). Responsibility for this presentation and interpretation lies with the authors of this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".