Bibliographic record
Abstract
Globally, around half the adult asthma population are current or former cigarette smokers. Cigarette smoking and asthma interact to induce an "asthma-smoking phenotype(s)," which has important implications for diagnosis, pathogenic mechanisms, and management. The lack of progress in understanding the effects of smoking on adults with asthma is due in part to their exclusion from most investigative studies and large clinical trials. In this review, we summarize the adverse clinical outcomes associated with cigarette smoking in asthma, highlight challenges in diagnosing asthma among cigarette smokers with chronic respiratory symptoms, particularly in older individuals with a long-standing smoking history, and review pathogenic mechanisms involving smoking- and asthma-related airway inflammation, tissue remodeling, corticosteroid insensitivity, and low-grade systemic inflammation. We discuss the key components of management including the importance of smoking cessation strategies, evidence for the effectiveness of the Global Initiative for Asthma recommendations on treatment in cigarette smokers, and the role of treatable traits such as type 2 eosinophilic airway inflammation. Lastly, we provide an algorithm to aid clinicians to manage current and former smokers with asthma. In the future, controlled and pragmatic trials in real-world populations should include cigarette smokers with asthma to provide an evidence base for treatment recommendations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".