Cutaneous Effects of Smoking
Bibliographic record
Abstract
BACKGROUND: Cigarette smoking is the single biggest preventable cause of death and disability in developed countries and is a significant public health concern. While known to be strongly associated with a number of cardiovascular and pulmonary diseases and cancers, smoking also leads to a variety of cutaneous manifestations. OBJECTIVE: This article reviews the effects of cigarette smoking on the skin and its appendages. METHODS: A literature review was based on a MEDLINE search (1966-2004) for English-language articles using the MeSH terms cutaneous, dermatology, tobacco, skin, and smoking. An additional search was subsequently undertaken for articles related to smoking and associated mucocutanous diseases, with the focus on pathogenesis and epidemiologic data. Articles presenting the highest level of evidence and latest reports were preferentially selected. RESULTS: Smoking is strongly associated with numerous dermatologic conditions including poor wound healing, wrinkling and premature skin aging, squamous cell carcinoma, psoriasis, hidradenitis suppurativa, hair loss, oral cancers, and other oral conditions. In addition, it has an impact on the skin lesions observed in diabetes, lupus, and AIDS. The evidence linking smoking and melanoma, eczema, and acne is inconclusive. Anecdotal data exist on the possible protective effects of smoking in oral/genital aphthosis of Behçet's disease, herpes labialis, pyoderma gangrenosum, acral melanoma, and Kaposi's sarcoma in AIDS patients. CONCLUSIONS: An appreciation of the adverse cutaneous consequences of smoking is important. Dermatologists can play an integral role in promoting smoking cessation by providing expert opinion and educating the public on the deleterious effects of smoking on the skin.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".