Tobacco Smoking: A Factor of Early Onset of Colorectal Cancer
Bibliographic record
Abstract
PURPOSE: Tobacco smoking is associated with a higher risk of developing colorectal cancer. This study was designed to assess the role of smoking in early onset of colorectal pathology. METHODS: This was a prospective cross-sectional study of 997 patients with colorectal cancer. Age of colorectal cancer diagnosis was studied in two groups of patients, i.e., smokers (>10 pack-years) and nonsmokers. Confounding factors, such as alcohol drinking, obesity, and gender, also were studied using a correlation analysis and multivariate logistic regression analysis. RESULTS: Of the 997 patients, 852 had sufficient data for analysis and were included. Baseline analysis showed that excluded patients had similar demographic characteristics. Smokers (n=108) reported symptoms related to colorectal cancer at an earlier mean age (64.1 (standard deviation, 11.7) years) than nonsmokers (69.6 (standard deviation, 12.6) years; mean difference, 5.5 (standard deviation, 1.2 years); P<0.001). Impact of smoking according to the bowel segment involved was significant for slow-transit segments (transverse and sigmoid colon and rectum). Multivariate analysis revealed that tobacco smoking was the only independent risk factor of early onset of colorectal cancers. CONCLUSIONS: Tobacco smoking could be a factor of early onset of colorectal cancers especially for slow-transit bowel segments. If these findings are confirmed in larger studies, screening for colorectal cancer should not involve a simple sigmoidoscopy but also an exploration of transverse colon in smokers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".