Lorlatinib Effectiveness and Quality-of-Life in Patients with ALK-Positive NSCLC Who Had Failed Second-Generation ALK Inhibitors: Canadian Real-World Experience
Bibliographic record
Abstract
Lorlatinib is the only targeted therapy approved in Canada to treat patients with anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) whose tumor has progressed despite treatment with second-generation ALK tyrosine kinase inhibitor (TKI), a patient population with high unmet need and lack of publicly reimbursed targeted treatments in Canada. We prospectively examined the real-world effectiveness and impact of lorlatinib on quality-of-life in 59 lorlatinib-treated patients, characterized as: median age of 62.0 years; 47.5% were female; 32.2% had central nervous system metastases; 50.8% had 2+ prior ALK TKI lines; and alectinib was the most common ALK TKI (72.9%) administered before lorlatinib, including 44.1% who received first-line alectinib. With a median follow-up of 15.3 months (IQR: 6.2–19.2), median time-to-treatment discontinuation of lorlatinib was 15.3 months (95% CI: 7.9–not reached), with 54.2% (95% CI: 40.8–65.9%) of patients without treatment discontinuation at 12 months. At baseline, the mean health utility score (HUS) was 0.744 (SD: 0.200). At 3 months, patients receiving lorlatinib demonstrated a 0.069 (95% CI: 0.020–0.118; p = 0.007) average HUS increase over baseline; HUS was maintained at 6 and 12 months. Thus, patients with ALK-positive NSCLC post second-generation ALK TKI remained on lorlatinib for a meaningful duration of time while their quality-of-life was preserved.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".