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Record W2901559050 · doi:10.2188/jea.je20180140

Premature Mortality Due to Malignancies of the Kidney and Bladder in Japan, 1980–2010

2018· article· en· W2901559050 on OpenAlexafffund
Truong‐Minh Pham, Tatsuhiko Kubo, Yoshihisa Fujino, Naohiro Fujimoto, Ikko Tomisaki, Akinori Minato, Shinya Matsuda

Bibliographic record

VenueJournal of Epidemiology · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple and Secondary Primary Cancers
Canadian institutionsAlberta Health Services
FundersUniversity of Occupational and Environmental HealthAlberta Health Services
KeywordsMedicinePopulationKidneyInternal medicineDemographyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: In the present study, we examined the trends of premature mortality due to kidney and bladder cancers among the Japanese population from 1980 through 2010. METHODS: Mortality data were obtained from the World Health Organization mortality database. Years of life lost (YLL) was estimated using Japanese life tables. Average lifespan shortened (ALSS) was calculated and defined as the ratio of years of life lost relative to the expected lifespan. RESULTS: Over the study period, the age-standardized rates to the World Standard Population for deaths from kidney and bladder cancers were stable. The average years of life lost (AYLL) measure shows decreases of about 4 and 6 years of life for kidney cancer in men and women, respectively, and decreases of about 2 years of life for bladder cancer in both sexes. The ALSS shows that patients with kidney cancer lost 21.0% and 24.7% of their lifespan among men and women in 1980, whereas respective losses were 15.3% and 15.8% in 2010. Also, patients with bladder cancer on average lost 13.5% in men and 14.2% in women in 1980 and 10.8% in men and 11.1% in women in 2010. CONCLUSIONS: Our study shows favorable trends in premature mortality for kidney and bladder cancers in Japan over a 30-year period; however, patients with bladder cancer on average lost a smaller proportion of their lifespan compared to those with kidney cancer. The development of a novel ALSS measure is convenient in examination of the burden of premature mortality over time.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.052
GPT teacher head0.342
Teacher spread0.290 · 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

Citations16
Published2018
Admission routes2
Has abstractyes

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