Is Previous Respiratory Disease a Risk Factor for Lung Cancer?
Bibliographic record
Abstract
RATIONALE: Previous respiratory diseases have been associated with increased risk of lung cancer. Respiratory conditions often co-occur and few studies have investigated multiple conditions simultaneously. OBJECTIVES: Investigate lung cancer risk associated with chronic bronchitis, emphysema, tuberculosis, pneumonia, and asthma. METHODS: The SYNERGY project pooled information on previous respiratory diseases from 12,739 case subjects and 14,945 control subjects from 7 case-control studies conducted in Europe and Canada. Multivariate logistic regression models were used to investigate the relationship between individual diseases adjusting for co-occurring conditions, and patterns of respiratory disease diagnoses and lung cancer. Analyses were stratified by sex, and adjusted for age, center, ever-employed in a high-risk occupation, education, smoking status, cigarette pack-years, and time since quitting smoking. MEASUREMENTS AND MAIN RESULTS: Chronic bronchitis and emphysema were positively associated with lung cancer, after accounting for other respiratory diseases and smoking (e.g., in men: odds ratio [OR], 1.33; 95% confidence interval [CI], 1.20-1.48 and OR, 1.50; 95% CI, 1.21-1.87, respectively). A positive relationship was observed between lung cancer and pneumonia diagnosed 2 years or less before lung cancer (OR, 3.31; 95% CI, 2.33-4.70 for men), but not longer. Co-occurrence of chronic bronchitis and emphysema and/or pneumonia had a stronger positive association with lung cancer than chronic bronchitis "only." Asthma had an inverse association with lung cancer, the association being stronger with an asthma diagnosis 5 years or more before lung cancer compared with shorter. CONCLUSIONS: Findings from this large international case-control consortium indicate that after accounting for co-occurring respiratory diseases, chronic bronchitis and emphysema continue to have a positive association with lung cancer.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".