Association of atopic dermatitis with tobacco smoke exposure: a systematic review and meta- analysis
Bibliographic record
Abstract
Previous studies found conflicting results about whether exposure to tobacco smoke is associated with increased atopic dermatitis (AD). We examined this association by systematic review and meta-analysis of MEDLINE, EMBASE, Scopus, and Cochrane Library and identified 86 studies, including 680,176 patients from 39 countries. A meta-analysis was performed using random-effects models to estimate pooled odds ratios (OR). Subset analyses were performed for different ages (children or adult), regions, study designs (cross-sectional vs. longitudinal), sizes (<5,000 or ≥5,000) and quality (Newcastle-Ottawa Score [NOS] <6 or ≥6), and amount of smoking (mild or extensive). Overall, 17,969 (12.9% [range 1.2–50.0%]) were active smokers, 33,200 (15.3% [range 0.9–56.8%]) were passively exposed to tobacco smoke in the home and 14,004 (15.4% [range 2.3–34.4%]) of children born to mothers who smoked during pregnancy, respectively, had a previous and/or current history of AD. Atopic dermatitis was associated with higher odds of active smoking (random-effects OR [95% CI]: 1.87 [1.32–2.63]) and exposure to passive smoke (1.18 [1.01–1.38]), but not maternal smoking during pregnancy (1.06 [0.80–1.40]). In sensitivity analyses, the association between active smoking and AD remained significant in children and adults, in all continents studied and study sizes, but all studies were cross-sectional designs and had a NOS score ≥6. Exposure to passive smoke was associated with AD in children and adults, cross-sectional studies, South/Central American and African studies, study size <5,000 and NOS <6. This study demonstrates that active smoking and passive exposure to smoke are associated with increased AD prevalence.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".