Relationship between cigarette smoking and risk of chronic myeloid leukaemia: a meta-analysis of epidemiological studies
Bibliographic record
Abstract
OBJECTIVE: Previous epidemiologic studies that have been reported on the association between cigarette smoking and risk of chronic myeloid leukaemia (CML) have remained controversial. A comprehensive meta-analysis was performed to evaluate smoking as a potential relationship factor and incidence of CML. METHODS: Systematic literatures collected from articles published before August 2015 were searched from PubMed, EMBASE and the Cochrane Library. A total of 10 studies (nine case-controls and one cohort) met inclusion criteria of this meta-analysis. Odds ratios (ORs) with 95% confidence interval (CI) were calculated to assess the strength of the association between cigarette smoking and risk of CML in this study. Quality assessments were performed on the studies with the Newcastle-Ottawa Scale. I2 index was used to evaluate heterogeneity. Finally, publication bias was assessed through funnel plots and Begger's test. RESULTS: No significant association was observed between ever-smokers and CML when compared among non-smokers (OR = 1.13, 95% CI: 0.99-1.29) or between subgroups stratified by smoking history, gender, geographical region, study design and source of patients. Our results demonstrate that this association was stronger in individuals who smoked <20 cigarettes/day (OR = 1.72, 95% CI: 1.06-2.79) vs. individuals who smoked >20 cigarettes/day (OR = 1.24, 95% CI: 0.55-2.81). Moreover, cumulative smoking of <15, 15-30 and >30 pack-years was associated with ORs of 1.22, 1.32 and 1.39, respectively (P < 0.001, for trend). CONCLUSION: This meta-analysis suggests that smoking may significantly increase the risk of CML in a dose-dependent manner. However, additional well-designed, prospective cohort studies are required to verify these findings and identify other risk factors associated with CML.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.055 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".