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Record W3198829064 · doi:10.1093/jjco/hyab139

International comparison of trends in cancer mortality: Japan has fallen behind in screening-related cancers

2021· article· en· W3198829064 on OpenAlexaboutno aff
Kota Katanoda, Yuri Ito, Tomotaka Sobue

Bibliographic record

VenueJapanese Journal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
FundersMinistry of Health, Labour and Welfare
KeywordsMedicineMortality rateCancerDemographyLiver cancerColorectal cancerStomach cancerBreast cancerInternal medicine

Abstract

fetched live from OpenAlex

While the age-standardized mortality rate in Japan is decreasing for all cancers as a whole, this is not the case for some major site-specific cancers. We descriptively compared trends in all-cancer and site-specific cancer mortality in Japan and selected countries. Data on age-standardized cancer mortality rates in six countries (Japan, the USA, the UK, Canada, Australia and the Republic of Korea) in 1980-2016 were obtained from the World Health Organization mortality database. While stomach and liver cancer mortality rates in Japan and Korea were initially much higher than those in non-Asian countries, they have rapidly decreased over the long term. By contrast, colorectal, pancreatic and cervical cancer mortality rates in Japan, which were initially lower than those in other countries, have increased such that they are now similar or higher than the rates in non-Asian countries. For male lung cancer, Japan's initially lower mortality rate is now comparable to that in non-Asian countries as a result of slower decline. Meanwhile, the mortality rate of female breast cancer in Japan and Korea has increased and is nearing the rates observed in non-Asian countries, which by contrast have shown a steady decrease. Thus, while Japan has been successful in reducing the burden of stomach and liver cancers, it is falling behind in reducing the mortality rate of screening-related cancers such as colorectal, female breast and cervical cancers. Control measures for these cancers need to be strengthened.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.413
GPT teacher head0.564
Teacher spread0.151 · 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.

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

Citations41
Published2021
Admission routes1
Has abstractyes

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