Prenatal and early-life exposure to traffic-related air pollution and allergic rhinitis in children: A systematic literature review
Bibliographic record
Abstract
BACKGROUND: Traffic-related air pollution (TRAP) is hypothesised to play a role in the development of allergic rhinitis (AR). Prenatal and early-life exposure to traffic-related air pollution is considered critical for later respiratory health. However, we could not find any articles systematically reviewing the risk of prenatal and early-life exposure to traffic-related air pollution for allergic rhinitis in children. METHODS: A systematic literature search of PubMed, Web of Science and Medline was conducted to identify studies focused on the association between prenatal and early-life exposure to TRAP and AR in children. Other inclusion criteria were: 1) original articles; 2) based upon prospective or retrospective studies or case-control studies; and 3) publications were restricted to English. Literature quality assessment was processed using the Newcastle-Ottawa Scale (NOS) evaluation scale. This systematic literature review has been registered on the prospero (crd.york.ac.uk/prospero) with the following registry number: CRD42022361179. RESULTS: Only eight studies met the inclusion criteria. The exposure assessment indicators included PM2.5, PM2.5 absorbance, PM10, NOx, CO, and black carbon. On the whole, exposure to TRAP during pregnancy and the first year of life were positively associated with the development of AR in children. CONCLUSIONS: This systematic review presents supportive evidence about prenatal and early-life exposure to TRAP and the risk of AR in children.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".