Ambient PM2.5 and its chemical constituents on lifetime-ever pneumonia in Chinese children: A multi-center study
Bibliographic record
Abstract
The long-term effects of ambient PM2.5 and chemical constituents on childhood pneumonia were still unknown. A cross-sectional study was conducted in 30,315 children in the China Children, Homes, Health (CCHH) project, involving 205 preschools in six cities in China, to investigate the long-term effects of PM2.5 constituents on lifetime-ever diagnosed pneumonia. Information on the lifetime-ever pneumonia and demographics were collected by validated questionnaires. The lifetime annual average ambient PM2.5, ozone and five main PM2.5 constituents, including SO42−, NO3−, NH4+, organic matter (OM) and black carbon (BC), were estimated according to preschool addresses by a combination of satellite remote sensing, chemical transport modeling and ground-based monitors. The prevalence of lifetime-ever diagnosed pneumonia was 34.5% across six cities and differed significantly among cities (p = 0.004). The two-level logistic regression models showed that the adjusted odds ratio for PM2.5 (per 10 µg/m3) and its constituents (per 1 µg/m3)-SO42−, NO3−, NH4+, and OM were 1.12 (95% CI:1.07–1.18), 1.02 (1.00–1.04), 1.06 (1.04–1.09), 1.05 (1.03–1.07) and 1.09 (1.06–1.12), respectively. Children in urban area, aged < 5 years and breastfeeding time < 6 months enhanced the risks of pneumonia. Our study provided robust results that long-term levels of ambient PM2.5 and its constituents increased the risk of childhood pneumonia, especially NH4+, NO3− and OM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".