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The Burden of Primary Liver Cancer and Underlying Etiologies From 1990 to 2015 at the Global, Regional, and National Level
Tomi Akinyemiju, Semaw Ferede Abera, Muktar Beshir Ahmed, Noore Alam, Mulubirhan Assefa Alemayohu, Christine A. Allen, Rajaa Al‐Raddadi, Nelson Alvis‐Guzmán, Yaw Ampem Amoako, Al Artaman, Tadesse Awoke Ayele, Aleksandra Barać, Isabela M. Benseñor, Adugnaw Berhane, Zulfiqar A Bhutta, Jacqueline Castillo-Rivas, Abdulaal Chitheer, Jee-Young Jasmine Choi, Lalit Dandona, Rakhi Dandona, Subhojit Dey, Daniel Dicker, Huyen Phuc, Donatus U. Ekwueme, Maysaa El Sayed Zaki, Florian Fischer, Thomas Fürst, Jamie Hancock, Simon I Hay, Peter J. Hotez, Sun Ha Jee, Amir Kasaeian, Yousef Khader, Young‐Ho Khang, G Anil Kumar, Michael Kutz, Heidi J. Larson, Alan D Lopez, Raimundas Lunevičius, Reza Malekzadeh, Colm McAlinden, Toni Meier, Walter Mendoza, Ali H. Mokdad, Maziar Moradi‐Lakeh, Gabriele Nagel, Quyen Le Nguyen, Grant Nguyen, Felix Akpojene Ogbo, George Patton, David M. Pereira, Farshad Pourmalek, Mostafa Qorbani, Amir Radfar, Gholamreza Roshandel, Joshua A. Salomon, Juan Sanabria, Benn Sartorius, Maheswar Satpathy, Monika Sawhney, Sadaf G Sepanlou, Katya Anne Shackelford, Hirbo Shore Roba, Jiandong Sun, Desalegn Tadese Mengistu, Roman Topór-Mądry, Bach Xuan Tran, Kingsley Nnanna Ukwaja, Vasily Vlassov, Theo Vos, Tolassa Wakayo, Elisabete Weiderpass, Andrea Werdecker, Naohiro Yonemoto, Mustafa Z Younis, Chuanhua Yu, Zoubida Zaidi, Liguo Zhu, Christopher J L Murray, Mohsen Naghavi, Christina Fitzmaurice
Bibliographic record
VenueJAMA Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of British ColumbiaHospital for Sick ChildrenSickKids FoundationUniversity of Manitoba
FundersDepartment of Global Health and Population, Harvard T.H. Chan School of Public HealthJohns Hopkins Bloomberg School of Public HealthRede de Química e TecnologiaWestern Sydney UniversityIngham Institute for Applied Medical ResearchUniwersytet Medyczny im. Piastów Slaskich we WroclawiuUniversität UlmLaboratório Associado para a Química VerdeGolestan University of Medical SciencesNational Research University Higher School of EconomicsTexas Children's HospitalUniversitetet i OsloUniversitetet i BergenJimma UniversityHaramaya UniversitySeoul National UniversityAlborz University of Medical SciencesMekelle UniversityQueensland University of TechnologyUniversität BielefeldPublic Health Foundation of IndiaKarolinska InstitutetYonsei UniversityAll-India Institute of Medical SciencesIran University of Medical SciencesWuhan UniversityJordan University of Science and TechnologyJohns Hopkins UniversityUniversity of WashingtonImperial College LondonInyuvesi Yakwazulu-NataliFogarty International CenterUniversidade do PortoUniwersytet Jagielloński Collegium MedicumCase Western Reserve UniversityTehran University of Medical Sciences and Health ServicesNorwegian Institute of Public HealthCenters for Disease Control and PreventionJackson State UniversityUniversitetet i TromsøUniversität BaselCollege of Medicine, Seoul National UniversitySabin Vaccine Institute
KeywordsMedicineYears of potential life lostLife expectancyLiver cancerCancerIncidence (geometry)Mortality rateDemographyDisease burdenStandardized mortality ratioPopulationEnvironmental healthInternal medicine
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.101
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 head0.171
GPT teacher head0.405
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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
Citations2,020
Published2017
Admission routes1
Has abstractyes