Quality of Care for People with Lung Cancer and Chronic Obstructive Pulmonary Disease: A Vulnerable and Overlooked Population
Bibliographic record
Abstract
This thesis evaluates the quality of care for people with lung cancer and coexisting chronic obstructive pulmonary disease (COPD). The three studies assess whether care for this vulnerable population is timely, effective, people-centered, integrated, and equitable, addressing five of the seven pillars for quality of care outlined by the World Health Organization (WHO). This research evaluates quality of care at the population level by integrating patient-reported outcome measures and data from health administrative databases and cancer registries in Ontario, Canada. First, the association between COPD and diagnosis of lung cancer in the early or advanced stages is quantified. Second, the impact of COPD on symptom burden in lung cancer patients is assessed. Third, the equitable and timely provision of palliative care for lung cancer patients with and without COPD is evaluated. Understanding the complexity of caring for lung cancer patients with comorbid COPD is essential to improving the quality of care which patients receive. This thesis demonstrates that COPD is very common, affecting over half of lung cancer patients in Ontario. Coexisting COPD impacts the stage of diagnosis, symptom burden, and receipt of palliative care, affecting patients and the care they receive. Patients with COPD were less likely to be diagnosed with lung cancer in the advanced stages compared to people without COPD. Lung cancer patients with coexisting COPD experienced more severe symptoms and had higher total symptom distress scores compared to patients without COPD. The differences in symptom burden were most pronounced among individuals diagnosed with early- stage disease. Early-stage lung cancer patients were more likely to receive palliative care if they had COPD. However, regardless of their COPD status, many patients with stage I – III lung cancer reported severe symptoms yet did not receive palliative care, highlighting unmet needs among this population. This thesis stresses the importance of considering coexisting COPD when caring for patients with lung cancer. It also improves our understanding of the burden of these two respiratory diseases on both the patients and the healthcare system. Integrated approaches to timely and effective healthcare for this vulnerable population are urgently needed, particularly those which prioritize 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".