Quality of Life Measurement in Cancer Patients Receiving Palliative Radiotherapy for Symptomatic Lung Cancer: A Literature Review
Bibliographic record
Abstract
Approximately 27% of North American cancer deaths are attributable to cancer of the lung. Many lung cancers are found at an advanced stage, rendering the tumours inoperable and the patients palliative. Common symptoms associated with palliative lung cancer include cough, hemoptysis, and dyspnea, all of which can significantly debilitate and diminish quality of life (QOL). In studies of the effects of cancer therapies, the frequent evaluative endpoints are survival and local control; however, it is imperative that clinical trials with palliative patients also have a QOL focus when a cure is unattainable. We conducted a literature review to investigate the use of QOL instrument tools in trials studying QOL or symptom palliation of primary lung cancer or lung metastases through the use of radiotherapy. We identified forty-three studies: nineteen used a QOL tool, and twenty-four examined symptom palliation without the use of a QOL instrument. The European Organization for Research and Treatment of Cancer (eortc) QLQ-C30 survey was the most commonly used QOL questionnaire (in thirteen of twenty trials). Of those thirteen studies, eight also incorporated the lung-specific QOL survey eortc QLQ-LC13 (or the eortc QLQ-LC17). A second lung-specific survey, the Functional Assessment of Cancer Therapy-Lung (fact-L) was used in only two of the twenty trials. In total, only ten of forty-three trials (23%) used a lung-specific QOL tool, suggesting that QOL was of low priority as an endpoint and that measures created for lung cancer patients are underused. We encourage investigators in future trials to include specific QOL instruments such as the eortc QLQ-LC13 or the fact-L for studies in palliative thoracic radiotherapy because those instruments provide a measure of QOL specific to patients with lung cancer or lung metastases.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".