Real-World Management and Outcomes of Crizotinib-Treated ROS1-Rearranged NSCLC: A Retrospective Canadian Cohort
Bibliographic record
Abstract
The use, safety and effectiveness of crizotinib as part of the management of ROS1-rearranged NSCLC patients in a real-world Canadian clinical cohort was the focus of this retrospective review. Twenty-one ROS1-rearranged patients with advanced/metastatic disease receiving crizotinib between 2014-2020 were identified; crizotinib demonstrated tolerability and effectiveness in this population where outcomes were similar to those described in other crizotinib-treated real-world cohorts, but lower than those of the PROFILE 1001 clinical trial population. Systemic anti-cancer therapy prior to crizotinib initiation occurred in half of the study cohort, with platin-pemetrexed and immune checkpoint inhibitors being most common. Platin-pemetrexed showed good effectiveness in this cohort, but despite high prevalence of upregulated PD-L1 expression, immune checkpoint inhibitors showed poor effectiveness in his cohort. Among all systemic therapies received, crizotinib showed the most effective disease control, although longer intervals between diagnosis and crizotinib initiation were more common among those showing a lack of clinical response to crizotinib, and patients with brain metastases at the time of crizotinib initiation also showed increased diagnosis to crizotinib initiation intervals and decreased clinical response to crizotinib. This study reveals crizotinib has clinical benefit, but timely identification of ROS1-rearrangements and initiation targeted therapies appears important to maximize outcome in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 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".