Podcast on Lorlatinib as a First-Line Treatment Option for Patients with ALK-Positive Metastatic NSCLC with Brain Metastasis
Bibliographic record
Abstract
Brain metastases are especially common in anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC), with a cumulative incidence of over 50% and associated with a poor prognosis, high symptom burden, and decreased quality of life. Lorlatinib is a brain-penetrant, third-generation ALK tyrosine kinase inhibitor (TKI), which has a high potency against resistance mutations seen with earlier generation ALK TKIs. In 2018, lorlatinib was granted accelerated approval in second- and third-line treatment for use in patients with ALK-positive metastatic NSCLC on the basis of phase 1/2 study results. This initial approval was expanded for first-line treatment of patients with ALK-positive metastatic NSCLC on the basis of the interim analysis of the phase 3 CROWN study showing longer progression-free survival, time to intracranial progression, duration of response, and objective response rate compared with crizotinib. This manuscript is a transcript of our podcast, in which we discuss the clinical significance of controlling the onset of brain metastases, considerations in selecting a first-line therapy option, efficacy and safety observed in patients with and without brain metastases, and rationales for using lorlatinib upfront versus reserving for a later line in therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".