Impact of COPD on lung cancer symptom burden: A population-based study
Bibliographic record
Abstract
Background: Chronic obstructive pulmonary disease (COPD) and lung cancer commonly coexist and have extensive symptom overlap. Aim: To determine whether lung cancer patients with COPD experience more severe symptom burden. Methods: We conducted a retrospective, population-based cohort study of stage I-IV lung cancer patients in Ontario, Canada who completed the Edmonton Symptom Assessment Scale (ESAS) within 90 days of lung cancer diagnosis. COPD was ascertained using a validated algorithm, with patients grouped as; no COPD, previously diagnosed COPD (at least 90 days prior to lung cancer diagnosis), and newly diagnosed COPD (within 90 days of lung cancer diagnosis). Multivariable modified Poisson regression analyses stratified by early (I/II) and advanced stage (III/IV) were used to determine the association between COPD status and any moderate to severe symptom (ESAS ≥ 4) and the number of moderate to severe symptoms. Linear regression analyses were used for total symptom distress, physical, and emotional symptom scores. Results: Among 38,898 lung cancer patients, 53% had COPD (previously diagnosed 81%, newly diagnosed 19%). Collectively, individuals with previously diagnosed COPD had the most severe symptom burden. Across all stages, both COPD groups had a significantly higher risk of experiencing any (RR 1.04 to 1.18) and multiple moderate to severe symptoms (RR 1.07 to 1.25), in addition to worse symptom distress and physical symptom scores (p < 0.0001). Differences were most pronounced among early-stage patients with previously diagnosed COPD. Conclusions: Lung cancer patients with underlying COPD have worse symptom burden, suggesting a greater need for integrated care that prioritizes symptom management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".