Waterpipe smoking and cancer: systematic review and meta-analysis
Bibliographic record
Abstract
<h3>Objective</h3> Although accumulating evidence suggests harmful effects of waterpipe smoking, there is limited information about its direct association with chronic diseases, notably cancer. We provide an up-to-date systematic review and meta-analysis of the association between waterpipe smoking and cancer. <h3>Data sources</h3> Systematic search of articles indexed in main biomedical databases: Pubmed, EmBase, Google Scholar and Web of Science, published between 1962 and September 2014. Search keywords included a combination of waterpipe or hookah, sheesha, nargile, hubble-bubble, goza or gaylan, and cancer. <h3>Study selection</h3> Focus on observational studies (cohort, case–control, cross-sectional) that evaluated the association between waterpipe smoking and cancer. Studies with mixed exposures excluded. <h3>Data extraction</h3> Two investigators independently extracted data and reached consensus on all items. <h3>Data synthesis</h3> 13 case–control studies met the inclusion criteria and were considered for meta-analysis. The methodological quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS). Meta-analysis revealed a positive association between waterpipe smoking and lung cancer (OR=4.58 (2.61 to 8.03); I<sup>2</sup>=44.67%), and oesophageal cancer (OR=3.63 (1.39 to 9.44); I<sup>2</sup> =94.49%). The majority of studies had a NOS score of 5–6 or 7, indicating ‘fair’ or ‘good’ quality, respectively. <h3>Conclusions</h3> Our findings support a positive association between waterpipe smoking and cancer risk. However, high-quality studies with standardised exposure measurements are needed to clarify the contribution of waterpipe smoking to chronic diseases. More investments in initiatives for surveillance, intervention and regulatory policy for waterpipe smoking are urgently warranted.
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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 | 0.024 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".