Lung Cancer Risk among Patients with Asthma–Chronic Obstructive Pulmonary Disease Overlap
Bibliographic record
Abstract
Abstract Rationale Chronic obstructive pulmonary disease (COPD) is a well-established independent risk factor for lung cancer; however, the literature on the association between asthma and lung cancer is mixed. Whether asthma–COPD overlap (ACO) is associated with lung cancer has not been studied. Objectives We aimed to compare lung cancer risk among patients with ACO versus COPD and other conditions associated with airway obstruction. Methods We studied 13,939 smokers from the National Lung Cancer Screening Trial who had baseline spirometry and used spirometric indices and history of childhood asthma to categorize participants into five specific airway disease subgroups. We used Poisson regression to compare unadjusted and adjusted lung cancer risk. Results The incidence rate of lung cancer per 1,000 person-years was as follows: ACO, 13.2 (95% confidence interval [CI], 8.1–21.5); COPD, 11.7 (95% CI, 10.5–13.1); asthmatic smokers, 1.8 (95% CI, 0.6–5.4); Global Initiative for Chronic Obstructive Lung Disease–Unclassified, 7.7 (95% CI, 6.4–9.2); and normal spirometry smokers, 4.1 (95% CI, 3.5–4.8). Patients with ACO had increased adjusted risk of lung cancer compared with patients with asthma (incidence rate ratio [IRR], 4.5; 95% CI, 1.3–15.8) and normal spirometry smokers (IRR, 2.3; 95% CI, 1.3–4.2) in models adjusting for other risk factors. Adjusted lung cancer incidence in patients with ACO and COPD were not found to be different (IRR, 1.2; 95% CI, 0.7–2.1). Conclusions The risk of lung cancer among patients with ACO is similar to those with COPD and higher than other groups of smokers. These results provide further evidence that COPD, with or without a history of childhood asthma, is an independent risk factor for lung cancer.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".