Demographic trends in the incidence of young-onset colorectal cancer: a population-based study
Bibliographic record
Abstract
BACKGROUND: Evidence is emerging that the incidence of colorectal cancer is increasing in young adults, but the descriptive epidemiology required to better understand these trends is currently lacking. METHODS: A population-based cohort study was carried out including all adults aged 20-49 years diagnosed with colorectal cancer in England between 1974 and 2015. Data were extracted from the National Cancer Registration and Analysis Service database using ICD-9/10 codes for colorectal cancer. Temporal trends in age-specific incidence rates according to sex, anatomical subsite, index of multiple deprivation quintile and geographical region were analysed using Joinpoint regression. RESULTS: A total of 56 134 new diagnoses of colorectal cancer were analysed. The most sustained increase in incidence rate was in the group aged 20-29 years, which was mainly driven by a rise in distal tumours. The magnitude of incident rate increases was similar in both sexes and across Index of Multiple Deprivation quintiles, although the most pronounced increases in incidence occurred in the southern regions of England. CONCLUSION: Colorectal cancer should no longer be considered a disease of older people. Changes in incidence rates should be used to inform future screening policy, preventative strategies and research agendas, as well as increasing public understanding that younger people need to be aware of the symptoms of colorectal cancer.
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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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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.
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".