Evaluation and Management of Dyslipidemia in Patients Treated with Lorlatinib
Bibliographic record
Abstract
The use of lorlatinib, an anaplastic lymphoma kinase (ALK) inhibitor for the treatment of ALK-positive metastatic non-small cell lung cancer, is associated with dyslipidemia in over 80% of patients. Clinical trial protocols for the management of lorlatinib-associated dyslipidemia differ from clinical practice guidelines for the management of dyslipidemia to prevent cardiovascular disease, in that they are based on total cholesterol and triglyceride levels rather than on the low-density lipoprotein cholesterol and non-high-density lipoprotein cholesterol levels that form the basis of current cardiovascular guideline recommendations. In order to simplify and harmonize the management of cardiovascular risk in patients with lorlatinib, an advisory committee consisting of a medical oncologist, a cardiologist, and two pharmacists with expertise in cardiology and oncology aimed to develop a simplified algorithm, adapted from the Canadian Cardiovascular Society dyslipidemia recommendations. Recommendations for the evaluation and management of hypercholesterolemia and isolated hypertriglyceridemia in patients treated with lorlatinib are outlined. These recommendations are based on data collected in a large number of lipid-lowering therapy trials applicable to individuals with and without cancer. Considering the relatively long life expectancy and improving prognosis of patients with ALK translocations, this specific patient population should be treated as are patients without cancer and are likely to derive the same benefits from lipid-lowering therapy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".