Lung cancer symptom appraisal among people with chronic obstructive pulmonary disease: A qualitative interview study
Bibliographic record
Abstract
OBJECTIVE: The incidence of lung cancer is four times higher in people with chronic obstructive pulmonary disease (COPD) compared with the general population. Promotion of a shorter time from symptom onset to presentation is one potential strategy for earlier lung cancer diagnosis, but distinguishing respiratory symptoms can be difficult. We investigated how the experience of COPD influences symptom appraisal and help seeking for potential lung cancer symptoms. METHODS: We conducted qualitative interviews with men (n = 17) and women (n = 23) aged 40 to 83 years with COPD. Topic guides drew on the integrated symptom-response framework and covered symptom experience, interpretation, action, recognition, help seeking, evaluation, and reevaluation. We used the framework method to analyse the data. RESULTS: Participants said that they attributed chest symptoms to their COPD; no other cause was considered. Participants said that family/friends noticed changes in their symptoms and encouraged help seeking. Others felt isolated by their COPD because they could not get out, were fatigued, or were embarrassed. Participants visited health professionals frequently, but increased risk of lung cancer was not discussed. CONCLUSIONS: Our study provides insight into different levels of influence on symptom appraisal and targets for intervention. Greater awareness of increased lung cancer risk and support to act on symptom changes is essential and could be achieved through a concerted information campaign. Health professionals working with people with COPD could also optimise appointments to support symptom appraisal of potential lung cancer symptoms.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".