Association between COPD and Stage of Lung Cancer Diagnosis: A Population-Based Study
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is associated with an increased risk of lung cancer; however, the association between COPD and stage of lung cancer diagnosis is unclear. We conducted a population-based cross-sectional analysis of lung cancer patients (2008-2020) in Ontario, Canada. Using estimated propensity scores and inverse probability weighting, logistic regression models were developed to assess the association between COPD and lung cancer stage at diagnosis (early: I/II, advanced: III/IV), accounting for prior chest imaging. We further examined associations in subgroups with previously diagnosed and undiagnosed COPD. Over half (55%) of all lung cancer patients in Ontario had coexisting COPD (previously diagnosed: 45%, undiagnosed at time of cancer diagnosis: 10%). Compared to people without COPD, people with COPD had 30% lower odds of being diagnosed with lung cancer in the advanced stages (OR = 0.70, 95% CI: 0.68 to 0.72). Prior chest imaging only slightly attenuated this association (OR = 0.77, 95% CI: 0.75 to 0.80). The association with lower odds of advanced-stage diagnosis remained, regardless of whether COPD was previously diagnosed (OR = 0.68, 95% CI: 0.66 to 0.70) or undiagnosed (OR = 0.77, 95% CI: 0.73 to 0.82). Although most lung cancers are detected in the advanced stages, underlying COPD was associated with early-stage detection. Lung cancer diagnostics may benefit from enhanced partnership with COPD healthcare providers.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".