Real-World Outcomes in Patients with Alk-Positive Non-Small Cell Lung Cancer Treated with Crizotinib
Bibliographic record
Abstract
Background: Crizotinib has shown greater efficacy in clinical trials than chemotherapy in patients with anaplastic lymphoma kinase-positive (ALK+) non-small cell lung cancer (NSCLC), but little information is available on its use and outcomes in real-world settings. We therefore assessed treatment patterns and outcomes in ALK+ NSCLC patients treated with crizotinib in regular clinical practice. Methods: A retrospective medical record review was conducted in North America for adults with ALK+ NSCLC treated with crizotinib as first- or later-line therapy for metastatic disease between 1 August 2011 and 31 March 2013 (for the United States) or 1 May 2012 and 31 March 2013 (for Canada). Crizotinib-related trial enrollees were excluded. Descriptive analyses were conducted to assess treatment patterns and objective response rate (ORR). Progression-free survival (PFS) and overall survival (OS) were descriptively analyzed using Kaplan-Meier methods. Results: Data were extracted for 212 patients in the United States (n = 147) and Canada (n = 65). Mean (standard deviation [SD]) age was 58.9 (9.5) years, and 69% were male. Seventy-nine patients (37%) were deceased at record abstraction. Sixty-five percent (n = 137) initiated crizotinib as first-line therapy. Mean (SD) duration of crizotinib treatment was 8.7 (4.9) months. Objective response rate was 66% (69% for first-line recipients, 60% for second-/later-line). Median (95% CI) PFS and OS from crizotinib initiation were 9.5 (8.7, 10.1) and 23.4 (19.5, −) months, respectively. One- and two-year survival probabilities were 82% and 49%, respectively. One- and two-year survival probabilities were 82% and 49%, respectively. Conclusions: Outcomes for crizotinib recipients in this study align with previous trials, with ORR appearing more favourable in first-line recipients. Our findings indicate that crizotinib outcomes in clinical studies may translate to regular clinical practice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".