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Record W4286487975 · doi:10.1093/jjco/hyac115

International variations in hepatic tumours incidence in children and adolescents

2022· article· en· W4286487975 on OpenAlexaboutno aff
Kumiko Saika, Laureline Gatellier

Bibliographic record

VenueJapanese Journal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple and Secondary Primary Cancers
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIncidence (geometry)PediatricsInternal medicine

Abstract

fetched live from OpenAlex

In order to compare the subtype distribution of hepatic tumours in children and adolescents between Japan and other countries, we extracted information on cancer incidence in children and adolescents from the third volume of the International Incidence of Childhood Cancer series (IICC-3) (1). The IICC-3 reports the number or incidence rates of cancers diagnosed in childhood and adolescence from cancer registries (regional or national) worldwide. We analyzed hepatic tumour incidence in four countries in Asia (Japan, China, the Republic of Korea and Thailand), two countries in Africa (Egypt and Uganda), four countries in the Americas (North: The USA and Canada, Latin and Caribbean: Brazil and Colombia), three countries in Europe (the UK, France and Germany) and two countries in Oceania (Australia and New Zealand). Information from the Republic of Korea, USA, UK, Australia and New Zealand were obtained at the national level and those from the other countries were extracted from one or multiple regional cancer registries. The years of incidence included in the analyses varied from country to country, ranging from 1990 to 2014, with the shortest being 12 years (Egypt: 1999–2010, UK: 2000–11) and the longest being 24 years (Japan and China, both: 1990–2013). In this study, we compared the incidence and proportional distribution of hepatic tumour subtype in children (0–14 years old) and adolescents (15–19 years old) between these countries. As shown in Table 1, hepatic tumours incidence rates in children (0–14 years old) were high in Asia, especially in China, followed by North American countries, Oceanian countries and European countries, and were lowest in African countries. The incidence rate in adolescents (15–19 years old) was the highest in Uganda, which was followed by China and Thailand. Other countries had incidence rates ranging from 0.5 to 1.8 cases per 1 000 000 person-years. Incidence rates of hepatic tumours in children and adolescents (per 1 000 000 person-years) Note: Data were extracted from the third volume of the International Incidence of Childhood Cancer series (IICC-3) (1). The table and figures are prepared by the authors of this article, and the responsibility for this presentation and its interpretation lies with the authors of this article. aAge-standardized incidence rate. Figure 1 shows the proportional distribution of subtype of hepatic tumour incidence in children. It is difficult to assess the distribution of subtype when the percentage of unspecified subtype is high: 60.4% in China, 38.0% in Thailand and 29.4% in Uganda. All other countries had the highest percentage of hepatoblastoma, with 90.8% in Japan, 82–89% in Egypt, the two North American countries, the three European countries and Australia; 73–74% in the Republic of Korea, Columbia and New Zealand. Brazil had the same highest percentage of hepatoblastoma, but it was 54.8%, while hepatic carcinoma accounted for 25.8%. Proportional distribution of subtype of hepatic tumours in children (0–14 years old). Proportional distribution of subtype of hepatic tumours in children (15–19 years old). Figure 2 shows the proportional distribution of subtype of hepatic tumours in adolescents. The percentage of unspecifies subtype were high in China, Thailand and Uganda, as was seen in children. Also in Egypt, unspecified accounted for 33.3% of the hepatic tumours, while in children, percentage of unspecified was 17.6%. Unlike in children, hepatic carcinoma accounted for the largest proportion of hepatic tumours in all countries. The percentage of hepatic carcinoma were 100% in New Zealand, 90–95% in Canada and the UK; 82–89% in the Republic of Korea, Brazil, the USA, France, Germany and Australia and 65–77% in Egypt, Uganda and Columbia. None declared.

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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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.015
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.038
GPT teacher head0.402
Teacher spread0.364 · 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.

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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