Effects of Symptom Burden on Quality of Life in Patients with Lung Cancer
Bibliographic record
Abstract
Lung cancer patients suffer from numerous symptoms that impact their quality of life. This study aims to identify the symptom burden on quality of life in lung cancer patients. This survey used a structured questionnaire to collect data from 8 March 2021 to 12 May 2021. Patient demographic information was collected. The data on symptom burden and quality of life (QOL) of patients were obtained from the QLQ-C30 and the QLQ-LC13. The stepwise multiple regression analysis was used to estimate lung cancer-related symptom burden in relation to quality of life. The study included 159 patients with lung cancer who completed the questionnaire. The mean age of the patients was 63.12 ± 11.4 years, and 64.8% of them were female. The Global Quality of Life score of the QLQ-C30 was 67.87 ± 22.24, and the top five lung cancer-related symptoms were insomnia, dyspnea, and fatigue from the QLQ-C30, and coughing and dyspnea from the QLQ-LC13. The multiple regression analysis showed that appetite loss was the most frequently associated factor for global QOL (β = −0.32; adjusted R2: 27%) and cognitive function (β = −0.15; adjusted R2: 11%), while fatigue was associated with role function (β = −0.35; adjusted R2: 43%), emotional function (β = −0.26; adjusted R2: 9%), and social function (β = −0.26; adjusted R2: 27%). Dyspnea was associated with physical function (β = −0.45; adjusted R2: 42%). Appetite loss, fatigue, and dyspnea were the main reasons causing symptom burdens on quality of life for lung cancer patients. Decreasing these symptoms can improve the quality of life and survival for patients with lung cancer.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".