Impact of crizotinib on patient-reported symptoms and quality of life (QOL) compared with single-agent chemotherapy in a phase III study of advanced <i>ALK</i>+ non-small cell lung cancer (NSCLC).
Bibliographic record
Abstract
8108 Background: PROFILE 1007 compared the efficacy and safety of the ALK inhibitor crizotinib (N=172) with that of standard-of-care chemotherapy (pemetrexed [PEM; N=99] or docetaxel [DOC; N=72]) in patients with advanced ALK+ NSCLC. The primary endpoint was progression-free survival. The main objective of our present post-hoc analyses was to compare patient-reported outcomes in the crizotinib arm with those of the DOC and PEM subgroups in the chemotherapy arm. Methods: Patient-reported outcomes were assessed at baseline, on day 1 of each cycle, and at the end of treatment using EORTC QLQ-C30 and lung cancer module QLQ-LC13. Higher scores (range 0−100) indicate higher symptom severity or better functioning/QOL. Time to deterioration (TTD) was defined as time from randomization to the earliest time with a ≥10-point increase from baseline for pain in chest, dyspnea, or cough and was compared between groups using an unstratified log-rank test. Repeated measures mixed-effects analyses were performed to compare change from baseline scores, with no adjustment for multiple comparisons. Results: Completion rates at baseline were ≥90% in each group and scores were well balanced. Crizotinib treatment was associated with a significantly longer TTD compared with PEM (median, 5.6 vs. 1.9 mo; HR, 0.66; 95% CI, 0.48−0.92; P=0.013) or DOC (median, 5.6 vs. 0.9 mo; HR, 0.37; 95% CI, 0.26−0.54; P<0.0001). A significantly greater improvement from baseline was observed with crizotinib compared with either the PEM or DOC subgroups for global QOL (P<0.01), cough (P<0.001), dyspnea (P<0.0001), pain in arm or shoulder (P<0.0001), pain in chest (P<0.0001), pain in other parts (P<0.01), fatigue (P<0.05), insomnia (P<0.05), and pain (P<0.0001). A significantly greater improvement was also observed with crizotinib compared with DOC for functioning (P<0.05), alopecia (P<0.0001), and hemoptysis (P<0.0001). Conclusions: Crizotinib treatment showed a significantly greater improvement from baseline in key patient-reported lung cancer symptoms and global QOL compared with DOC and PEM, in addition to improved efficacy previously reported. Clinical trial information: NCT00932893.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".