ONLINE FIRST Smoking and the Risk of Nonmelanoma Skin Cancer Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objective: To perform a systematic review and metaanalysis to collate evidence of the effects of smoking on the risk of nonmelanoma skin cancer. Data Sources: We searched 4 electronic databases (from inception to October 2010) and scanned the reference lists of the publications retrieved to identify eligible comparative epidemiologic studies. Study Selection: Titles, abstracts, and full text were assessed independently by 2 authors against prespecified inclusion/exclusion criteria. Data Extraction: Data were extracted and quality was assessed independently by 2 authors using the Newcastle-Ottawa Scale. Data Synthesis: Meta-analysis was performed using random-effects models. Results are presented as odds ratios (ORs) with 95 % CIs. Heterogeneity was assessed using I 2. Twenty-five studies were included. Smoking was significantly associated with cutaneous squamous cell carcinoma (OR, 1.52; 95 % CI, 1.15-2.01; I 2 =64%; 6 studies). Smoking was not significantly associated with basal cell carcinoma (OR, 0.95; 95 % CI, 0.82-1.09; I 2 =59%; 14 studies) or nonmelanoma skin cancer (OR, 0.62; 95 % CI, 0.21-1.79; I 2 =34%; 2 studies). Conclusion: This study clearly demonstrates that smoking increases the risk of cutaneous squamous cell carcinoma; however, smoking does not appear to modify the risk of basal cell carcinoma.
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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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.009 | 0.007 |
| 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) | 0.007 | 0.001 |
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".