Combined effect of modifiable and non-modifiable risk factors for colorectal cancer risk in a pooled analysis of 11 population-based studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it