Colorectal Cancer Incidence Trends in the United States and United Kingdom: Evidence of Right- to Left-Sided Biological Gradients with Implications for Screening
Bibliographic record
Abstract
Several lines of evidence support the premise that screening colonoscopy reduces colorectal cancer (CRC) incidence, but there may be differential benefits for right- and left-sided tumors. To better understand the biological basis of this differential effect, we derived biomathematical models of CRC incidence trends in U.S. and U.K. populations, representing relatively high- and low-prevalence screening, respectively. Using the Surveillance Epidemiology and End Results (SEER) and the Office for National Statistics (ONS) registries (both 1973-2006), we derived stochastic multistage clonal expansion (MSCE) models for right-sided (proximal colon) and left-sided (distal colon and rectal) tumors. The MSCE concept is based on the initiation-promotion-progression paradigm of carcinogenesis and provides a quantitative description of natural tumor development from the initiation of an adenoma (via biallelic tumor suppressor gene inactivation) to the clinical detection of CRC. From 1,228,036 (SEER: 340,582; ONS: 887,454) cases, parameter estimates for models adjusted for calendar-year and birth-cohort effects showed that adenoma initiation rates were higher for right-sided tumors, whereas, paradoxically, adenoma growth rates were higher for left-sided tumors. The net effect was a higher cancer risk in the right colon only after age 70 years. Consistent with this finding, simulations of adenoma development predicted that the relative prevalence for right- versus left-sided tumors increases with increasing age, a differential effect most striking in women. Using a realistic biomathematical description of CRC development for two nationally representative registries, we show age- and sex-dependent biological gradients for right- and left-sided colorectal tumors. These findings argue for an age-, sex-, and site-directed approach to CRC screening.
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 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".