Symptom response in non-small cell lung cancer (NSCLC) patients (pts) treated with erlotinib: Quality of life analysis of the NCIC CTG BR.21 trial
Bibliographic record
Abstract
7018 Background: Erlotinib (Tarceva) prolongs survival in previously treated NSCLC pts (ASCO 2004). In the palliative setting, assessing symptoms and quality of life (QOL) is also important. Methods: BR.21 was a double blind placebo controlled phase III trial which randomized 731 pts with stage IIIB or IV NSCLC, who had failed 1st or 2nd line chemotherapy, to erlotinib 150 mg/day PO or placebo, in a 2:1 ratio. Primary endpoint was survival; QOL was a secondary endpoint, as assessed by EORTC QLQ-C30 and the lung cancer module at baseline and q 4 weeks until progression. Primary endpoint for QOL analysis was time to deterioration of cough, dyspnea and pain. In addition, mean changes from baseline in QOL domains and symptoms were compared between the arms, as well as proportions of patients improving/deteriorating. Results: Compliance to QOL completion was high (87% at baseline, >70% in follow up for >12 cycles). Both arms had comparable baseline QOL, with pts showing greatest impairments in global QOL (mean score 53–55), role (59–60) and physical functioning (64–65), and greatest symptom burden for fatigue (42–45), cough (43–39), dyspnea (35–36) and pain (34–38). 451 pts were included in primary symptom analysis for cough, 522 for dyspnea, and 527 for pain. Pts on erlotinib had clinically and statistically significantly longer time to deterioration of tumor-related symptoms: 4.9 vs 3.68 mo for cough (p=0.04), 4.7 vs 2.89 mo for dyspnea (p=0.01), and 2.79 vs 1.91 mo for pain (p=0.02). These findings were confirmed by analysis of proportion of pts with >10 point improvement in these symptoms (44%, 34% and 30% respectively, all significantly different from placebo). Differences in QOL domains were seen in the physical function (31% improved on erlotinib vs 19% on placebo, p 0.01), and in global QOL (35% vs 26%, p<0.01). Analysis of change over time revealed that there was a general trend towards improvement in most symptoms and QOL domains in erlotinib arm (with the exception of diarhea), but deterioration in the placebo arm. Conclusions: Erlotinib improves survival in previously treated pts with NSCLC, as well as tumor-related symptoms and some aspects of QOL. Author Disclosure Employment or Leadership Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration OSI OSI
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".