Adverse Side Effects of Crizotinib in the Treatment of Anaplastic Lymphoma Kinase-Mutated Non-small Cell Lung Cancer: A Systematic Review
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer deaths worldwide, with the majority consisting of non-small cell lung cancer (NSCLC). Genetic mutations present an opportunity for targeted therapy, in addition to current mainstay treatments such as chemotherapy and radiotherapy. Overall, 5% of NSCLCs have an anaplastic lymphoma kinase (ALK) mutation, often prevalent in a younger population. Crizotinib is an ALK inhibitor that was approved to treat ALK-mutated advanced NSCLC. While common side effects such as nausea, fatigue, and diarrhea are mostly well tolerated, adverse side effects can lead to treatment discontinuation or adjustment or can be fatal. This systematic review used articles searched on Google Scholar and PubMed which were assessed using the Cochrane risk-of-bias tool and Newcastle-Ottawa Scale. This yielded nine papers consisting of randomized controlled trials and cohort studies. Side effects resulting in cessation of treatment or dose reduction included liver dysfunction, nausea, neutropenia, and QT prolongation. This review showed that crizotinib has a better side effect profile than chemotherapy in ALK-positive NSCLC, even though toxicities leading to treatment withdrawal are present. Adverse effects were tackled by dose reduction, temporary withdrawal from treatment, and close monitoring.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".