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Record W4413453410 · doi:10.1093/jnci/djaf238

Increase of early-onset colorectal cancer: a cohort effect

2025· article· en· W4413453410 on OpenAlexaboutno aff
Laura Downham, Mathieu Laversanne, Sandra Pérdomo, Adalberto Miranda‐Filho, Freddie Bray, Paul Brennan

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2025
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
FundersCentre International de Recherche sur le CancerWorld Health Organization
KeywordsColorectal cancerMedicineCohortCancerOncologyInternal medicine

Abstract

fetched live from OpenAlex

Increasing incidence rates of early-onset colorectal cancer (ie, <50 years of age) have been reported across multiple countries. We investigated long-term cancer incidence data from 1995 or earlier from Australia, Canada, England, and the United States separately by sex. Estimated annual percentage change and age-period-cohort models were used to assess trends by country and sex. All countries showed increasing early-onset colorectal cancer incidence in successive birth cohorts since 1960, with individuals born in the 1990s facing at least 4-fold higher risks than individuals born in the 1960s. Cohort effects were observed across all countries, with sharper increases at younger ages. Over the most recent decade, the estimated annual percentage change ranged from 3.4% in Australia and the United States to 4.5% in England, with steep rises before age 40 years. The emergence of these trends from ages 20 to 29 years suggests that contributing factors may originate early in life and reflect exposures whose effect begin in youth and accumulate throughout the lifespan.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.053
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.333
Teacher spread0.316 · 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.

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

Citations10
Published2025
Admission routes1
Has abstractyes

Explore more

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