Smoking as a risk factor for colorectal neoplasms in young individuals? A systematic meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Early-onset colorectal neoplasms (EoCRN) include both benign and malign colorectal tumors, which occur before the age of 50. The incidence of EoCRN is rising worldwide. Tobacco smoking has previously been proven to be related to the development of various tumor types. However, its relationship with EoCRN is not clearly defined. Hence, we carried out a systematic review and a meta-analysis to evaluate the relationship between smoking status and the risk of EoCRN. METHODS: A systematic search of PubMed, EMBASE, and Web of Science up to September 7, 2022, was performed for studies that evaluated the association of smoking status with EoCRN. The quality of the case-control study was evaluated with the Newcastle‒Ottawa Scale. The quality of the cross-sectional studies was evaluated with the American Health Care Research and Quality checklist. Fixed-effects models were used to pool odds ratios (ORs) to evaluate the relationship between the risk of developing EoCRN and smoking status. The meta-analyses were performed with Review Manager version 5.4, and funnel plots and publication bias tests were produced by STATA software. RESULTS: A total of six studies were included in this meta-analysis. After pooling the results of these six studies, we found that current smokers carry a relatively high risk of developing EoCRN (OR, 1.33; 95% confidence interval [CI], 1.17-1.52) compared to never-smokers. Ex-smokers were not at a significantly increased risk for developing EoCRN (OR, 1.00; 95% CI, 0.86-1.18). DISCUSSION: Smoking behavior is significantly associated with an increased risk for developing EoCRN and might be one of the reasons for the increasing incidence. Ex-smokers who quit are not at significant risk of developing EoCRN.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.045 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".