Combined effect of modifiable and non-modifiable risk factors for colorectal cancer risk in a pooled analysis of 11 population-based studies
Bibliographic record
Abstract
OBJECTIVE: 'Environmental' factors associated with colorectal cancer (CRC) risk include modifiable and non-modifiable variables. Whether those with different non-modifiable baseline risks will benefit similarly from reducing their modifiable CRC risks remains unclear. DESIGN: Using 7945 cases and 8893 controls from 11 population-based studies, we combined 17 risk factors to characterise the overall environmental predisposition to CRC (environmental risk score (E-score)). We estimated the absolute risks (ARs) of CRC of 10 and 30 years across E-score using incidence-rate data from the Surveillance, Epidemiology, and End Results programme. We then combined the modifiable risk factors and estimated ARs across the modifiable risk score, stratified by non-modifiable risk profile based on genetic predisposition, family history and height. RESULTS: , 1.33; 95% CI 1.30 to 1.37). Across E-scores, 30-year ARs of CRC increased from 2.5% in the lowest quartile (Q1) to 5.9% in the highest (Q4) quartile for men, and from 2.1% to 4.5% for women. The modifiable risk score had a stronger association in those with high non-modifiable risk (relative excess risk due to interaction=1.2, 95% CI 0.5 to 1.9). For those in Q4 of non-modifiable risk, a decrease in modifiable risk reduced 30-year ARs from 8.9% to 3.4% for men and from 6.0% to 3.2% for women, a level lower or comparable to the average population risk. CONCLUSIONS: Changes in modifiable risk factors may result in a substantial decline in CRC risk in both sexes. Those with high inherited risk may reap greater benefit from lifestyle modifications. Our results suggested comprehensive evaluation of environmental factors may facilitate CRC risk stratification.
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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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.035 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".