Effect of smoking on melanoma incidence: a systematic review with meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is a strong correlation between cigarette smoking and the development of many cancer types. It is therefore paradoxical that multiple reports have suggested a reduced incidence of melanoma in smokers. This study aimed to analyze all existing studies of melanoma incidence in smokers relative to nonsmokers. METHODS: Searches of MEDLINE and Embase were conducted for studies reporting data on melanoma in smokers and never-smokers. No study design limitations or language restrictions were applied. The outcome examined was the association between smoking status and melanoma. Analyses focused on risk of melanoma in smokers and never-smokers generated from multivariable analyses, and these analyses were pooled using a fixed-effects model. Risk of bias was assessed using the Newcastle-Ottawa tool. RESULTS: Forty-nine studies that included 59 429 patients with melanoma were identified. Pooled analyses showed statistically significant reduced risks of melanoma in male smokers (risk ratio [RR] = 0.60, 95% confidence interval [CI] = 0.56 to 0.65, P < .001) and female smokers (RR = 0.79, 95% CI = 0.73 to 0.86, P < .001). Male former smokers had a 16% reduction in melanoma risk compared with male never-smokers (RR = 0.84, 95% CI = 0.77 to 0.93, P < .001), but no risk reduction was observed in female former smokers (RR = 1.0, 95% CI = 0.92 to 1.08). CONCLUSIONS: Current smokers have a statistically significant reduced risk of developing melanoma compared with never-smokers, with a reduction in melanoma risk of 40% in men and 21% in women.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".