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Record W4200042406 · doi:10.1093/jjco/hyab199

Age-specific incidence rate of leukaemia in the world

2021· article· en· W4200042406 on OpenAlexaboutno aff
Ryoko Machii, Kumiko Saika

Bibliographic record

VenueJapanese Journal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIncidence (geometry)ChinaDemographyCancer registryCancerCancer incidenceDeveloped countryEnvironmental healthGeographyPopulationInternal medicine

Abstract

fetched live from OpenAlex

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.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.129
GPT teacher head0.467
Teacher spread0.338 · 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 teacher head, not a consensus.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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