Smoking and the Risk of Nonmelanoma Skin Cancer
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic review and meta-analysis 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 I2. Twenty-five studies were included. Smoking was significantly associated with cutaneous squamous cell carcinoma (OR, 1.52; 95% CI, 1.15-2.01; I2 = 64%; 6 studies). Smoking was not significantly associated with basal cell carcinoma (OR, 0.95; 95% CI, 0.82-1.09; I2 = 59%; 14 studies) or nonmelanoma skin cancer (OR, 0.62; 95% CI, 0.21-1.79; I2 = 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".