Association between smoking and colorectal cancer in Eastern Mediterranean Regional Office (EMRO): A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Smoking poses a significant risk for colorectal cancer (CRC), considered the third leading reason for cancer-related deaths worldwide. However, there has been limited research on the relationship between smoking and CRC in the Eastern Mediterranean Regional Office (EMRO). Therefore, a meta-analysis was conducted to combine available data and gain a comprehensive understanding of the relationship between smoking and CRC in EMRO. Methods: Two independent researchers searched PubMed, Scopus, and Web of Science until December 2022. The included studies were checked for risk of bias administering the Newcastle-Ottawa scale. Heterogeneity was evaluated using I2 statistics and the Cochrane test. Publication bias was determined through funnel plot analysis and Egger's regression test. Additionally, a meta-regression analysis explored the impact of a country's Human Development Index (HDI) on the relationship between smoking and CRC. Results: The final analysis included 26 studies, revealing a significant association between smoking and CRC (OR = 1.40; 95% CI: 1.11 - 1.78; P = 0.004). Moreover, smoking had a more pronounced adverse effect on CRC in countries with higher HDIs compared to those with lower HDIs (OR = 1.30; 95% CI: 0.99 - 1.71; P = 0.054). Conclusions: Our findings underscore the importance of implementing smoking cessation programs and policies in EMRO countries, as they demonstrate a positive relationship between smoking and the risk of CRC. Furthermore, the results suggest that a country's level of human development may influence the association between smoking and CRC. Further research is needed to investigate this potential connection and develop targeted public health interventions.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".