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Enregistrement W3192287945 · doi:10.1093/jjco/hyab138

Age-specific kidney and other urinary organs’ cancer incidence rate in the world

2021· article· en· W3192287945 sur OpenAlexaboutno aff
Ayako Okuyama, Kumiko Saika

Notice bibliographique

RevueJapanese Journal of Clinical Oncology · 2021
Typearticle
Langueen
DomaineHealth Professions
ThématiqueGlobal Health Care Issues
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineIncidence (geometry)CancerKidney cancerCancer registryChinaDemographyGeographyInternal medicine

Résumé

récupéré en direct d'OpenAlex

In order to make a comparison of the age-specific kidney and other urinary organs’ 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 cancerv 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 (USA, Canada and Brazil), two countries in Oceania (Australia and New Zealand) and four countries in Europe (UK, France, 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 were from 2008 to 2012. Kidney and other urinary organs’ cancer were coded as C64-C66 and C68 based on ICD-10. Age-specific kidney and other urinary organs’ cancer incidence rate per 100 000 people in male. Age-specific kidney and other urinary organs’ cancer incidence rate per 100 000 people in female. Figure 1 shows the age-specific incidence rates of kidney and other urinary organs’ cancer in males by 5-year age groups for the selected countries. In general, cancer incidence rates of kidney and other urinary organs are higher in Europe, North America and Oceania, compared with countries in Asia. The incidence rates show a sharp increase until 70s in all the countries. In Europe, the trends of age-specific incidence rates are quite similar, that the rates become stable over 70 years of age. The age-specific incidence rates of the countries in America and Oceania also show similar trends, but the rate in Brazil is relatively low. When we look at the trends in Asia, trends of incidence rates in Japan, the Republic of Korea and China show increase until the age of 75–79 or 80–84 and then decrease or become stable, whereas that of Thailand shows increase with age and that of India shows high incidence rate only for those over 80 years of age compared with the other age groups. Figure 2 shows the age-specific incidence rates of kidney and other urinary organs’ cancer in females by 5-year age groups for the selected countries. In all the countries, the incidence rates for females are similar to those for males until the age of 40, and the rates for males are 1.5–2 times higher than that for females after the age of 40. Overall, cancer incidence rates were higher in Europe, America and Oceania, compared with the countries in Asia which is similar to the trend of males. In Europe, the trends of the age-specific incidence rates are similar among the selected countries. Incidence rates in Brazil tend to be lower than those in other countries in America and Oceania. In regards to the trends in Asia, the incidence rates for females in Japan and the Republic of Korea are higher than trends of other Asian countries. The incidence rates for female in Japan continue to increase with the age, whereas those in other countries increase until the age of 70 and then become stable as the same as trends of males. Note: Data were downloaded from the Global Cancer Observatory (GCO), 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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,006
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,386
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0100,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,246
Tête enseignante GPT0,582
Écart entre enseignants0,335 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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