Exposure to Poly- and Perfluoroalkyl Substances During Pregnancy and Asthma in Childhood: A Systematic Review
Bibliographic record
Abstract
Asthma is a chronic respiratory disease that has been increasing in prevalence among children globally. While genetic factors contribute to asthma risk, there are growing concerns about environmental exposures, such as poly- and perfluoroalkyl substances (PFAS), which may disrupt respiratory and immune development in the prenatal period. This systematic review aimed to assess the association between prenatal PFAS exposure and the development of asthma in childhood, integrating findings from epidemiological studies. A comprehensive literature search was conducted on Scopus, PubMed, and Google Scholar using predefined keywords. The review included prospective cohort studies examining prenatal PFAS exposure and asthma outcomes in children. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). A total of 13 studies were included in the final analysis. The studies involved diverse countries/populations with over 21,000 mother-child pairs. Although some studies reported associations between specific PFAS compounds and asthma, most found no significant relationship between prenatal PFAS exposure and doctor-diagnosed asthma. The findings were inconsistent, with null or mixed results across studies. Despite mechanistic evidence suggesting that PFAS could contribute to asthma development, epidemiological data do not consistently support a link between prenatal exposure and childhood asthma. Further research with standardized exposure measurements and larger cohort studies is needed to clarify these associations.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".