Asthma and Chronic Obstructive Pulmonary Disease Overlap in Women. Incidence and Risk Factors
Bibliographic record
Abstract
RATIONALE: Women with asthma are at a high risk of developing chronic obstructive pulmonary disease (COPD) or asthma and COPD overlap syndrome (ACOS) as they age, which is a condition associated with a high mortality rate, low quality of life, and high healthcare costs. However, factors influencing the development of ACOS remain unclear. OBJECTIVES: To quantify the risk of developing COPD in women in Ontario with asthma and identify factors that are associated with increased risk. METHODS: Data for women in Ontario with asthma who participated in the Canadian National Breast Screening Study from 1980 to 1985 were linked to health administrative databases, and participants were followed from 1992 to 2015. A competing risks survival model was used to measure the associations between sociodemographic, lifestyle, and environmental risk factors and time to COPD incidence, accounting for death as a competing risk. RESULTS: A total of 4,051 women with asthma were included in the study, of whom 1,701 (42.0%) developed COPD. The mean age at the study end date was 79 years. Low education, high body mass index, rurality, and high levels of cigarette smoking were associated with ACOS incidence, whereas exposure to fine particulate matter, a major air pollutant, was not. CONCLUSIONS: Individual risk factors appear to play a more significant role in the development of ACOS in women than environmental factors, such as air pollution. Prevention strategies targeting health promotion and education may have the potential to reduce ACOS incidence in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".