Inhaled corticosteroids and the risk of lung cancer in COPD: a population-based cohort study
Bibliographic record
Abstract
Inhaled corticosteroids (ICSs) are often prescribed in patients with chronic obstructive pulmonary disease (COPD). Their impact on the risk of lung cancer, a leading cause of mortality in COPD patients, remains uncertain.Population-based linked administrative data between the years 1997 and 2007 from the province of British Columbia, Canada, were used to evaluate the association between lung cancer risk and ICS use in COPD patients. COPD was defined on the basis of receipt of three COPD-related prescriptions in subjects ≥50 years of age. Exposure to ICS was incorporated into multivariable Cox regression models using several time-dependent methods ("ever" exposure, cumulative duration of use, cumulative dose, weighted cumulative duration of use and weighted cumulative dose).There were 39 676 patients who met the inclusion criteria. The mean±sd age of the cohort was 70.7±11.1 years and 53% were female. There were 994 (2.5%) cases of lung cancer during follow-up. In the reference case analysis (time-dependent "ever" exposure), ICS exposure was associated with a 30% reduced risk of lung cancer (HR 0.70 (95% CI 0.61-0.80)). ICS exposure was associated with a decrease in the risk of lung cancer diagnosis over all five methods of quantifying exposure.This population-based study suggests that ICS use reduces the risk of lung cancer in COPD patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".