A pragmatic guide for management of adverse events associated with lorlatinib
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lorlatinib is a brain-penetrant, third-generation tyrosine kinase inhibitor (TKI) indicated for the treatment of anaplastic lymphoma kinase (ALK)-positive metastatic non-small cell lung cancer (NSCLC). In clinical trials, lorlatinib has shown durable efficacy and a manageable safety profile in treatment-naive patients and in those who have experienced progression while receiving first- and/or second-generation ALK TKIs. Lorlatinib has a distinct safety profile from other ALK TKIs, including hyperlipidemia and central nervous system effects. Clinical trial data showed that most adverse events (AEs) can be managed effectively or reversed with dose modifications (such as dose interruptions or reductions) or with concomitant medications without compromising clinical efficacy or quality of life for patients. A pragmatic approach to managing AEs related to lorlatinib is required. We present patient-focused recommendations for the evaluation and management of select AEs associated with lorlatinib developed by clinicians and nurses with extensive lorlatinib expertise in routine clinical practice. The recommendations follow the general framework of "prepare, monitor, manage, reassess" to streamline AE management and assist in practical, actionable, and personalized patient care.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it