Continuation of Lorlatinib in ALK-Positive NSCLC Beyond Progressive Disease
Bibliographic record
Abstract
INTRODUCTION: Lorlatinib, a potent, selective third-generation ALK tyrosine kinase inhibitor (TKI), exhibited overall and intracranial antitumor activity in patients with ALK-positive NSCLC. METHODS: Retrospective analyses in the ongoing phase 2 trial (NCT01970865) investigated the clinical benefit of continuing lorlatinib beyond progressive disease (LBPD). Patients with previous crizotinib treatment as the only ALK TKI were group A (n = 28); those with at least one previous second-generation ALK TKIs were group B (n = 74). LBPD was defined as greater than 3 weeks of lorlatinib treatment after investigator-assessed progressive disease. Only patients with the best overall response of complete or partial response or stable disease were included. RESULTS: There were no major differences in baseline characteristics between groups. The median duration of treatment for patients who continued LBPD was 32.4 months (group A) and 16.4 months (group B) versus 12.5 months (group A) and 7.7 months (group B) for patients who did not continue LBPD. The median overall survival in group A was not reached (NR) in patients who continued LBPD versus 24.4 months (95% confidence interval [CI]: 12.1-NR); group B's median was 26.5 months (95% CI: 18.7-35.5) in patients who continued LBPD versus 14.7 months (95% CI: 9.3-38.5) in patients who did not continue LBPD. The median overall survival postprogression for groups A and B was NR (95% CI: 21.4-NR) and 14.6 months (95% CI: 11.2-19.2) in patients who continued LBPD and 8.0 months (95% CI: 1.5-NR) versus 5.3 months (95% CI: 2.8-14.3) in patients who did not continue LBPD. CONCLUSIONS: Continuing LBPD is a viable treatment strategy for select patients with ALK-positive NSCLC who progressed on lorlatinib.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".