Rash with the multitargeted kinase inhibitors nilotinib and dasatinib: meta‐analysis and clinical characterization
Bibliographic record
Abstract
OBJECTIVES: Nilotinib and dasatinib are second-generation tyrosine kinase inhibitors approved for the treatment of chronic myeloid leukemia (CML). In clinical trials, they have both been reported to cause rash in a significant number of patients, but its incidence varies significantly and has not been characterized clinically or histologically. The aim of this study was to determine the incidence of rash with nilotinib and dasatinib, and to provide a clinical and histopathological description of the rash. METHODS: We conducted a meta-analysis of clinical trials evaluating nilotinib and dasatinib to determine and compare the incidence of rash with these medications. Additionally, we performed a retrospective chart review to analyze the clinical presentation and histology of patients presenting with rash. RESULTS: The incidence of all-grade (grade 1-4) rash with nilotinib was 34.3% (95% CI, 27.9-41.3), higher (P = 0.017) than with dasatinib (23.3%; 95% CI, 18.8-28.6). Similarly, the incidence of high-grade rash with nilotinib (2.6%; 95% CI, 2.1-3.4) was higher (P = 0.002) than with dasatinib (1.1%; 95% CI, 0.8-1.6). The clinical presentation often consisted of a pruritic, perifollicular hyperkeratotic, occasionally erythematous papular rash affecting most areas of the body, depending on the severity. CONCLUSIONS: Both nilotinib and dasatinib are associated with rash in a significant number of patients. Further studies to prevent and treat rash with nilotinib and dasatinib are required to improve patient quality of life, adherence with therapy and oncologic outcome.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.036 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".