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Record W3121152532 · doi:10.1093/jjco/hyab007

Age-specific incidence rates of gallbladder cancer in the world

2021· article· en· W3121152532 on OpenAlexaboutno aff
Eiko Saito, Mariko Niino

Bibliographic record

VenueJapanese Journal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicCholangiocarcinoma and Gallbladder Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGallbladder cancerIncidence (geometry)GallbladderCancerInternal medicineCancer incidenceOncologyGastroenterologyGeneral surgery

Abstract

fetched live from OpenAlex

In order to make a comparison of the age-specific cancer incidence rate of gallbladder and other parts of biliary tract (hereinafter, referred to as gallbladder cancer) 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 (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 all the 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. Gallbladder cancer was coded as C23–C24 based on International Classification of Diseases 10th Revision.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.157
GPT teacher head0.476
Teacher spread0.319 · 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 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJapanese Journal of Clinical OncologySame topicCholangiocarcinoma and Gallbladder Cancer StudiesFrench-language works237,207