Progression from Asthma to Chronic Obstructive Pulmonary Disease. Is Air Pollution a Risk Factor?
Bibliographic record
Abstract
RATIONALE: Individuals with asthma-chronic obstructive pulmonary disease (COPD) overlap syndrome (ACOS), have more rapid decline in lung function, more frequent exacerbations, and poorer quality of life than those with asthma or COPD alone. Air pollution exposure is a known risk factor for asthma and COPD; however, its role in ACOS is not as well understood. OBJECTIVES: To determine if individuals with asthma exposed to higher levels of air pollution have an increased risk of ACOS. METHODS: Individuals who resided in Ontario, Canada, aged 18 years or older in 1996 with incident asthma between 1996 and 2009 who participated in the Canadian Community Health Survey were identified and followed until 2014 to determine the development of ACOS. Data on exposures to fine particulate matter (PM2.5) and ozone (O3) were obtained from fixed monitoring sites. Associations between air pollutants and ACOS were evaluated using Cox regression models. MEASUREMENTS AND MAIN RESULTS: Of the 6,040 adults with incident asthma who completed the Canadian Community Health Survey, 630 were identified as ACOS cases. Compared with those without ACOS, the ACOS population had later onset of asthma, higher proportion of mortality, and more frequent emergency department visits before COPD diagnosis. The adjusted hazard ratios of ACOS and cumulative exposures to PM2.5 (per 10 μg/m(3)) and O3 (per 10 ppb) were 2.78 (95% confidence interval, 1.62-4.78) and 1.31 (95% confidence interval, 0.71-2.39), respectively. CONCLUSIONS: Individuals exposed to higher levels of air pollution had nearly threefold greater odds of developing ACOS. Minimizing exposure to high levels of air pollution may decrease the risk of ACOS.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".