The Epidemiology of Chronic Obstructive Pulmonary Disease among Never Smokers in Ontario
Bibliographic record
Abstract
Objectives: Describe the epidemiology and risk factors for chronic obstructive pulmonary disease (COPD) in never smokers.Methodology: Health administrative databases linked to survey data in Ontario from 2000 – 2019 were used to identify individuals with COPD and their smoking status. Annual COPD prevalence, incidence, and health service utilization (HSU) rates were compared between ever and never smokers. Multivariable regression models were constructed to evaluate risk factors for never smoking COPD. Results: COPD was identified in 28 271 individuals (18.7%), of whom 7101 (25.1%) were never smokers. Prevalence of never-smoking-related COPD increased over time. Never smokers with COPD had similar rates of HSU compared to those who smoke. Predictors of COPD in never smokers included a history of asthma, environmental tobacco smoke exposure, lower socioeconomic status, increased comorbidity index and obesity. Conclusion: This uniquely large cohort identifies independent risk factors for and highlights substantial burden of COPD among never-smokers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".