Inhaled corticosteroid use and the incidence of lung cancer in COPD
Bibliographic record
Abstract
BACKGROUND: Inhaled corticosteroids (ICS) are suggested for potential chemoprevention of lung cancer. Several observational studies in patients with chronic obstructive pulmonary disease (COPD) reported inconsistent results, either significant reductions in lung cancer incidence with ICS use or no effect. We assessed this association, using an approach that avoided biases affecting some of the studies. METHODS: A cohort of patients with COPD, new users of long-acting bronchodilators over 2000-2014, was formed using the Quebec healthcare databases, and followed until 2015 for a first diagnosis of lung cancer. A 1-year delay after cohort entry was used to avoid protopathic bias and a 1-year latency period was included after the initiation of ICS use. A time-dependent Cox regression model was used to estimate the hazard ratio (HR) of lung cancer associated with ICS exposure, adjusted for covariates. RESULTS: The cohort involved 63 276 subjects, including 63% receiving ICS, with 3743 lung cancers occurring during a mean follow-up of 5 years. The adjusted HR of lung cancer associated with any ICS exposure was 1.01 (95% CI 0.94-1.08), relative to no ICS use. The HR with longer time (>4 years) since ICS initiation was 0.92 (95% CI 0.83-1.03), while with higher mean daily ICS dose (>1000 μg fluticasone equivalents) was 1.36 (95% CI 1.03-1.81). CONCLUSIONS: Inhaled corticosteroid use is not associated with a reduction in lung cancer incidence in patients with COPD. Observational studies reporting such reduction may have been affected by time-related biases and the inclusion of patients with asthma. The proposition of a randomised trial warrants some caution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".