Early-Life Ozone Exposure and Childhood Allergic Rhinitis: Critical Exposure Windows, Exposure-Response Relationships, and Protective Modifiers
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Childhood allergic rhinitis (AR) is associated with ozone (O 3 ) exposure, yet the sensitive exposure window (SEW) remains unclear. This multicenter survey of 38,176 children aged 3–6 years across 7 Chinese cities (2019–2020) estimated satellite-based individual maximum daily 8 h average O 3 exposure. The SEW and its exposure–response ( E – R ) relationship were assessed. Doctor-diagnosed AR prevalence was 11.9%. Average O 3 exposure from prenatal to AR onset ranged from 67.5 to 76.7 μg/m 3 . The critical SEW was identified as infancy, specifically 30–38 weeks postnatal (7.5–9.5 months). Per interquartile range (IQR) (10.1 μg/m 3 ) increase in O 3, the adjusted odds ratio was 1.29 (95% CI: 1.22–1.36), independent of PM 2 . 5 . The E – R relationship was nonlinear, J-shaped, and threshold-free. The SEW effect was stronger in southern cities and mitigated by ≥6 months of exclusive breastfeeding. In conclusion, O 3 significantly increases AR risk, with 7.5–9.5 months postnatal being a critical SEW, especially in exclusive breastfeeding <6 months and southern regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".