LUNG INFLAMMATION AMONG CHILDREN WITH ASTHMA EXPOSED TO INDUSTRIAL AND TRAFFIC POLLUTION
Bibliographic record
Abstract
Background and Aims: Children with asthma are susceptible to the effects of ambient air pollution, including increased symptoms and lung inflammation. Industrial emissions are an important source of air pollution in many areas. Oil refinery emissions are of particular concern as they comprise a complex mixture of organic and inorganic pollutants. We examined associations between fractional exhaled nitric oxide (FeNO), an indicator of airway inflammation, and exposure to oil refinery and traffic emissions on the health of children with asthma in Montreal, Canada. Methods: We recruited 69 children (age 8-13 years) with a physician diagnosis of asthma from schools and an asthma clinic. Subjects participated in the panel study for 10 consecutive days between October 2009 and April 2010. We measured personal exposures to sulphur dioxide (SO2) using Ogawa passive samplers (Ogawa & Company, Pompano Beach, FL, USA) and fine particulate matter (PM2.5) using Harvard Personal Environmental Monitors (HPEM, BGI, MA, USA) and continuously using the personal DataRAM (pDR-1200, MIE Inc, Bedford, MA). Filters were also analyzed for metals associated with oil refinery emissions. We recorded online FeNO daily using the NIOX MINO monitor (Aerocrine, Solna, Sweden) and collected participants’ reports on health, medication use, and activities. Linear mixed-effects regression models with autoregressive correlation structure were used to estimate the association between FeNO and pollutant exposure. Results: The geometric mean of FeNO was 20.9 ppb (geometric standard deviation: 2.2). Mean (SD) personal exposure to PM2.5 was 9.5 (13.4) µg/m3, while for SO2 it was 0.81 (3.21) ppb. Preliminary models indicate that an increase of 10 µg/m3 in previous 8-hour personal exposure to PM2.5 was associated with a 1% (95% CI: 0.1-2.0%) increase in FeNO, adjusted for corticosteroid use, age and sex. Conclusions: Preliminary results indicate an association between personal exposures to PM2.5 and increased airway inflammation in children with asthma.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".