Symptom Improvement in Lung Cancer Patients Treated With Erlotinib: Quality of Life Analysis of the National Cancer Institute of Canada Clinical Trials Group Study BR.21
Bibliographic record
Abstract
PURPOSE: This report describes the quality of life (QOL) findings of a randomized placebo controlled study of erlotinib, an epidermal growth factor receptor inhibitor, in patients with non-small-cell lung cancer (NSCLC). PATIENTS AND METHODS: This double-blind phase III trial randomly assigned 731 patients with NSCLC who had progressed after prior chemotherapy to erlotinib 150 mg daily or placebo, with survival as the primary study outcome. QOL was assessed by European Organisation for Research and Treatment of Cancer QLQ-C30 and the lung cancer module QLQ-LC13. The primary end points for QOL analysis were time to deterioration of three common lung cancer symptoms: cough, dyspnea, and pain. RESULTS: Survival was significantly longer (hazard ratio, 0.70; P < .0001) in the erlotinib arm. Compliance with QOL was 87% at baseline and more than 70% during treatment. Patients receiving erlotinib had significantly longer median time to deterioration for all three symptoms (4.9 v 3.7 months for cough [P = .04]; 4.7 v 2.9 months for dyspnea [P = .04], and 2.8 v 1.9 months for pain [P = .03]). QOL response analyses showed that 44%, 34%, and 42% of patients receiving erlotinib had improvement in these three symptoms, respectively. This was accompanied by a significant improvement in the physical function (31% erlotinib v 19% placebo, P = .01), and global QOL (35% v 26%, P < .0001). Patients with complete or partial response were more likely to have improvement in the QOL response than patients with stable or progressive disease (P < .01). CONCLUSION: Erlotinib not only improves survival in previously treated patients with NSCLC, but also improves tumor-related symptoms and important aspects of QOL.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".