Maternal tobacco exposure during pregnancy and atopic dermatitis in offspring: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract The main purpose of this review was to examine the evidence of the relationship between active smoking or passive smoking during pregnancy and atopic dermatitis in offspring. The protocol was written following the PRISMA Checklist and was registered in the PROSPERO database (registration number CRD42022381136). We implemented a comprehensive search in PubMed, Embase and Web of Science databases to identify all potentially related articles from inception through 1 December 2022. We assessed cohort studies and case–control studies using the Newcastle–Ottawa Scale (NOS), and the Joanna Briggs Institute (JBI) critical appraisal tool to assess the quality of cross‐sectional studies. Heterogeneity was investigated by using Cochrane Q tests and I2 statistics. In addition, according to the research design, population source and population size, the reasons for the heterogeneity were analysed. A total of 15 observational studies were included in this analysis. Our meta‐analysis suggests that atopic dermatitis in offspring is not associated with active smoking during pregnancy (pooled OR, 0.96 [95% CI 0.86–1.07]); however, it is related to passive smoking (OR, 1.52 [95% CI 1.36–1.70]). Passive smoking during pregnancy is associated with an increased risk of eczema development in offspring. More research is needed to explore the risk of active smoking and eczema development in offspring, especially the association between measurements of pregnancy cotinine levels in maternal body fluids and AD in offspring.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".