Inclusion body myositis in a patient with chronic myeloid leukemia treated with dasatinib: a case report
Bibliographic record
Abstract
INTRODUCTION: Chronic myelogenous leukemia is often treated using tyrosine kinase inhibitors such as dasatinib. Here we describe a rare case of inflammatory myopathy in a patient with chronic myelogenous leukemia treated with the tyrosine kinase inhibitor dasatinib. CASE PRESENTATION: A 69-year-old Caucasian man with imatinib-resistant chronic myelogenous leukemia achieved complete molecular remission in response to dasatinib therapy. However, from a normal initial serum creatine kinase level, he developed elevated serum creatine kinase levels and gradual-onset progressive muscle weakness after dasatinib therapy was initiated. Our patient was eventually diagnosed with inclusion body myositis. However, we were unable to determine the mechanism underlying the dasatinib-associated muscle weakness. Given the efficacy of dasatinib in the treatment of chronic myelogenous leukemia and our patient's mild symptoms of inclusion body myositis, he continued to receive dasatinib under close clinical and laboratory observation. CONCLUSION: Despite the wide use of dasatinib and its documented safety, we report a case of severe muscle injury of unknown etiology. Therefore, patients with chronic myelogenous leukemia receiving dasatinib and perhaps all tyrosine kinase inhibitors should be carefully monitored for signs of muscle injury, especially if this is associated with significant elevations in serum creatine kinase levels.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".