Treatment Patterns and Survival in Patients with ALK-Positive Non-small-Cell Lung Cancer: A Canadian Retrospective Study
Bibliographic record
Abstract
Background Crizotinib was the first agent approved for the treatment of anaplastic lymphoma kinase (ALK)– positive (+) non-small-cell lung cancer (NSCLC), followed by ceritinib. However, patients eventually progress or develop resistance to crizotinib. With limited real-world data available, the objective of the present work was to evaluate treatment patterns and survival after crizotinib in patients with locally advanced or metastatic ALK+ NSCLC in Canada. Methods In this retrospective study at 6 oncology centres across Canada, medical records of patients with locally advanced or metastatic ALK+ NSCLC were reviewed. Demographic and clinical characteristics, treatments, and outcomes data were abstracted. Analyses focused on patients who discontinued crizotinib treatment. Results Of the 97 patients included, 9 were crizotinib-naïve, and 39 were still receiving crizotinib at study end. The 49 patients who discontinued crizotinib treatment were included in the analysis. Of those 49 patients, 43% received ceritinib at any time, 20% subsequently received systemic chemotherapy only (but never ceritinib), and 37% received no further treatment or died before receiving additional treatment. Median overall survival from crizotinib discontinuation was shorter in patients who did not receive ceritinib than in those who received ceritinib (1.7 months vs. 20.4 months, p < 0.001). In a multivariable analysis, factors associated with poorer survival included lack of additional therapies (particularly ceritinib), male sex, and younger age, but not smoking status; patients of Asian ethnicity showed a nonsignificant trend toward improved survival. Conclusions A substantial proportion of patients with ALK+ NSCLC received no further treatment or died before receiving additional treatment after crizotinib. Treatment with systemic agents was associated with improved survival, with ceritinib use being associated with the longest survival.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".