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Record W3117572444 · doi:10.1093/jjco/hyaa253

Age-specific prostate cancer incidence rate in the world

2020· article· en· W3117572444 on OpenAlexaboutno aff
Megumi Hori, Matthew Palmer

Bibliographic record

VenueJapanese Journal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicMultiple and Secondary Primary Cancers
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstate cancerIncidence (geometry)OncologyCancerProstateInternal medicine

Abstract

fetched live from OpenAlex

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).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.170
GPT teacher head0.460
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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Same venueJapanese Journal of Clinical OncologySame topicMultiple and Secondary Primary CancersFrench-language works237,207