Respiratory Exacerbations Increase with Chronic PM <sub>2.5</sub> Exposure in Current and Former Smokers
Bibliographic record
Abstract
Abstract Rationale Short-term exposure to fine particulates (PM 2.5 ) transiently increases the risk of respiratory exacerbations, but the contribution of chronic, long-term particulate exposure to respiratory exacerbations is poorly defined. Objectives To assess long-term effects of PM 2.5 exposure on risk of severe respiratory exacerbations. Methods A longitudinal cohort of current and former smokers with and without COPD were surveyed every six months for severe exacerbation events. PM 2.5 concentrations at participant addresses were estimated using satellite, reanalysis, and ground-based monitoring data sources. Measurements and Main Results The relative risk of severe exacerbation increased by a factor of 1.516 (CI: 1.226, 1.873; p = 0.00012) for every 10 μg/m 3 increase in long-term PM 2.5 exposure across all participants. The effect in the non-COPD participants was greater, with a relative risk of 2.639 (CI: 1.840, 3.756; p<0.0001). Significant effect modifiers with greater effect of PM 2.5 , included prior exacerbations, female sex, and neighborhood characteristics and as well as smoking status, white race, disease severity, asthma diagnosis, and age at enrollment. Significant positive associations for PM 2.5 on exacerbations were identified at levels below the EPA primary annual standard for PM 2.5 of 9.0 μg/m 3 . Conclusions Persistent exposure to fine particulates is a significant risk factor for severe respiratory exacerbations in current and former smokers, and in patients with or at risk of COPD. The effect of fine particulates on the risk of severe exacerbations appears to be greater in those current and former smokers without COPD. The EPA annual PM 2.5 standard may be inadequate to prevent ongoing lung injury.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".