Bibliographic record
Abstract
To make a comparison of the age-specific prostate 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, 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 the all 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. Prostate cancer was coded as C61 based on ICD-10. Figure 1 shows the age-specific prostate cancer incidence rate by countries studied. For all regions the incidence of prostate cancer in men under 40 years was very low, and there was a steep increase in incidence after 40 years old. The incidence in Asian countries (except China and India), America and Oceania and Europe peaked at ages ~75–84, 70–79 and 70–79 years, respectively, and declined thereafter. The overall incidence for Asia tended to be lower than the incidences in other regions. Among countries in Asia, Japan and Republic of Korea had a higher incidence compared with other Asian countries. The peak incidence in Japan and South Korea was ~520 (per 100 000) and 370 (per 100 000), respectively. The incidence trend was similar for America and Oceania and Europe. As for America and Oceania, Brazil and Australia tended to have higher incidences compared with other countries, with peak incidences of ~980 (per 100 000) and 960 (per 100 000), respectively. Within Europe, France showed a higher incidence rate compared with other countries in Europe, with the peak incidence of 910 (per 100 000).
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.004 | 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".