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Enregistrement W4233651632 · doi:10.1016/s2468-1253(19)30347-4

The global, regional, and national burden of pancreatic cancer and its attributable risk factors in 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

2019· article· en· W4233651632 sur OpenAlexfundno aff
Akram Pourshams, Sadaf G Sepanlou, Kevin S Ikuta, Catherine Bisignano, Saeid Safiri, Gholamreza Roshandel, Mehdi Sharif, Morteza Khatibian, Christina Fitzmaurice, Molly R Nixon, Nooshin Abbasi, Mohsen Afarideh, Elham Ahmadian, Tomi Akinyemiju, Fares Alahdab, Shazia Alam, Vahid Alipour, Christine A. Allen, Nahla Anber, Alireza Ansari‐Moghaddam, Jalal Arabloo, Alaa Badawi, Mojtaba Bagherzadeh, Yaschilal Muche Belayneh, Belete Biadgo, Ali Bijani, Antonio Biondi, Tone Bjørge, Antonio Maria Borzì, Cristina Bosetti, Andrey Briko, Н. И. Брико, Giulia Carreras, Félix Carvalho, Jee-Young Jasmine Choi, Dinh‐Toi Chu, Anh Kim Dang, Ahmad Daryani, Dragoş Virgil Daviţoiu, Gebre Teklemariam Demoz, Rupak Desai, Subhojit Dey, Hoa Do, Huyen Phuc, Aziz Eftekhari, Alireza Esteghamati, Farshad Farzadfar, Eduarda Fernandes, Irina Filip, Florian Fischer, Masoud Foroutan, Mohamed M. Gad, Silvano Gallus, Birhanu Geta, Giuseppe Gorini, Nima Hafezi‐Nejad, James Harvey, Milad Hasankhani, Amir Hasanzadeh, Soheil Hassanipour, Simon I Hay, Hagos D Hidru, Chi Linh Hoang, Sorin Hostiuc, Mowafa Househ, Olayinka Stephen Ilesanmi, Milena Ilić, Seyed Sina Naghibi Irvani, Nader Jafari Balalami, Spencer L James, Farahnaz Joukar, Amir Kasaeian, Tesfaye Kassa, André Pascal Kengne, Rovshan Khalilov, Ejaz Ahmad Khan, Amir M Khater, Fatemeh Khosravi Shadmani, Jonathan Kocarnik, Hamidreza Komaki, Ai Koyanagi, Vivek Kumar, Carlo La Vecchia, Platon D Lopukhov, Farzad Manafi, Navid Manafi, Ana-Laura Manda, Fariborz Mansour‐Ghanaei, Dhruv Mehta, Varshil Mehta, Toni Meier, Hagazi Gebre Meles, Getnet Mengistu, Tomasz Miazgowski, Mehdi Mohamadnejad, Abdollah Mohammadian-Hafshejani, Milad Mohammadoo-Khorasani, Shafiu Mohammed, Farnam Mohebi, Ali H. Mokdad, Lorenzo Monasta, Maryam Moossavi, Rahmatollah Moradzadeh, Gurudatta Naik, Ionuţ Negoi, Cuong Tat Nguyen, Long Hoang Nguyen, Trang Huyen Nguyen, Andrew T Olagunju, Tinuke O Olagunju, Alyssa Pennini, Mohammad Rabiee, Navid Rabiee, Amir Radfar, Mahdi Rahimi, Goura Kishor Rath, David Laith Rawaf, Salman Rawaf, Robert C. Reiner, Nima Rezaei, Aziz Rezapour, Anas M. Saad, Seyedmohammad Saadatagah, Amirhossein Sahebkar, Hamideh Salimzadeh, Abdallah M Samy, Juan Sanabria, Arash Sarveazad, Monika Sawhney, Mario Šekerija, P. I. Shabalkin, Masood Ali Shaikh, Rajesh Sharma, Sara Sheikhbahaei, Reza Shirkoohi, Sudeep K Siddappa Malleshappa, Mekonnen Sisay, Kjetil Søreide, Sergey Soshnikov, Rasoul Sotoudehmanesh, Vladimir I. Starodubov, Michelle Subart, Rafael Tabarés‐Seisdedos, Degena Bahrey Tadesse, Eugenio Traini, Bach Xuan Tran, Khanh Bao Tran, Irfan Ullah, Marco Vacante, Amir Vahedian‐Azimi, Elena A. Varavikova, Ronny Westerman, Dawit Dawit Zewdu Wondafrash, Rixing Xu, Naohiro Yonemoto, Vesna Zadnik, Zhi‐Jiang Zhang, Reza Malekzadeh, Mohsen Naghavi

Notice bibliographique

Revue˜The œLancet. Gastroenterology & hepatology · 2019
Typearticle
Langueen
DomaineMedicine
ThématiquePancreatic and Hepatic Oncology Research
Établissements canadiensnon disponible
Organismes subventionnairesĐại học Quốc gia Hà NộiApplied Molecular Biosciences UnitI.M. Sechenov First Moscow State Medical UniversityUniversity of GondarUniversity of TabrizUniversidade do PortoBaki Dövlət UniversitetiBabol University of Medical SciencesBaqiyatallah University of Medical SciencesNational Center of Neurology and PsychiatryIslamic Azad UniversityUniversitetet i BergenLaboratório Associado para a Química VerdeGolestan University of Medical SciencesSeoul National University HospitalSeoul National UniversityUniversity of TorontoBauman Moscow State Technical UniversityFundação para a Ciência e a TecnologiaBill and Melinda Gates FoundationTabriz University of Medical SciencesPublic Health AgencyMansoura UniversityPublic Health Agency of CanadaMcGill UniversityFuel Cell Technologies ProgramSharif University of TechnologyDuke Global Health Institute, Duke UniversityTrường Đại học Duy TânIran University of Medical SciencesWuhan UniversityInstituto de Salud Carlos IIIMinistério da Ciência, Tecnologia e Ensino SuperiorRede de Química e TecnologiaGeneralitat ValencianaUniversity of WashingtonUniversità di Catania
Mots-clésMedicinePancreatic cancerIncidence (geometry)DemographyCancerEnvironmental healthDisease burdenCancer registryMortality rateBurden of diseaseDiseaseBody mass indexGerontologyInternal medicinePopulation

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Worldwide, both the incidence and death rates of pancreatic cancer are increasing. Evaluation of pancreatic cancer burden and its global, regional, and national patterns is crucial to policy making and better resource allocation for controlling pancreatic cancer risk factors, developing early detection methods, and providing faster and more effective treatments. METHODS: Vital registration, vital registration sample, and cancer registry data were used to generate mortality, incidence, and disability-adjusted life-years (DALYs) estimates. We used the comparative risk assessment framework to estimate the proportion of deaths attributable to risk factors for pancreatic cancer: smoking, high fasting plasma glucose, and high body-mass index. All of the estimates were reported as counts and age-standardised rates per 100 000 person-years. 95% uncertainty intervals (UIs) were reported for all estimates. FINDINGS: In 2017, there were 448 000 (95% UI 439 000-456 000) incident cases of pancreatic cancer globally, of which 232 000 (210 000-221 000; 51·9%) were in males. The age-standardised incidence rate was 5·0 (4·9-5·1) per 100 000 person-years in 1990 and increased to 5·7 (5·6-5·8) per 100 000 person-years in 2017. There was a 2·3 times increase in number of deaths for both sexes from 196 000 (193 000-200 000) in 1990 to 441 000 (433 000-449 000) in 2017. There was a 2·1 times increase in DALYs due to pancreatic cancer, increasing from 4·4 million (4·3-4·5) in 1990 to 9·1 million (8·9-9·3) in 2017. The age-standardised death rate of pancreatic cancer was highest in the high-income super-region across all years from 1990 to 2017. In 2017, the highest age-standardised death rates were observed in Greenland (17·4 [15·8-19·0] per 100 000 person-years) and Uruguay (12·1 [10·9-13·5] per 100 000 person-years). These countries also had the highest age-standardised death rates in 1990. Bangladesh (1·9 [1·5-2·3] per 100 000 person-years) had the lowest rate in 2017, and São Tomé and Príncipe (1·3 [1·1-1·5] per 100 000 person-years) had the lowest rate in 1990. The numbers of incident cases and deaths peaked at the ages of 65-69 years for males and at 75-79 years for females. Age-standardised pancreatic cancer deaths worldwide were primarily attributable to smoking (21·1% [18·8-23·7]), high fasting plasma glucose (8·9% [2·1-19·4]), and high body-mass index (6·2% [2·5-11·4]) in 2017. INTERPRETATION: Globally, the number of deaths, incident cases, and DALYs caused by pancreatic cancer has more than doubled from 1990 to 2017. The increase in incidence of pancreatic cancer is likely to continue as the population ages. Prevention strategies should focus on modifiable risk factors. Development of screening programmes for early detection and more effective treatment strategies for pancreatic cancer are needed. FUNDING: Bill & Melinda Gates Foundation.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,013
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,072
Score d'incertitude au seuil0,142

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,013
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,010
Bibliométrie0,0080,014
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
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 Opus0,040
Tête enseignante GPT0,345
Écart entre enseignants0,305 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
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

Citations672
Publié2019
Routes d'admission1
Résumé présentoui

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