P.022 Myasthenia gravis following dabrafenib and trametinib for metastatic melanoma
Bibliographic record
Abstract
Background: Inhibitors of BRAF and MEK, enzymes in the mitogen-activated protein kinase (MAPK) pathway, are now widely used in the treatment of metastatic melanoma. We report a case of acetylcholine receptor (AChR) antibody-positive myasthenia gravis developing after exposure to dabrafenib, a BRAF inhibitor, and trametinib, a MEK inhibitor. Methods: A 68-year-old man presented with dysarthria, dysphagia, cough, dyspnea, and fever. Examination revealed fatigable ptosis and proximal muscle weakness. He had started dabrafenib and trametinib for metastatic melanoma two weeks prior. He was diagnosed with myasthenia gravis and superimposed aspiration pneumonia. AChR antibodies were positive. Dabrafenib and trametinib were stopped. He improved rapidly with pyridostigmine alone, and remained free of myasthenic symptoms for the next two months. Another course of dabrafenib and trametinib was given, and seven weeks later, his myasthenic symptoms recurred. Pyridostigmine produced only partial improvement, and treatment with intravenous immunoglobulin and prednisone was initiated. Results: We are unaware of prior reports of an association between BRAF/MEK inhibitors and seropositive myasthenia gravis. The development of myasthenic symptoms twice after BRAF/MEK inhibitor exposure suggests that the association is more than coincidental. Conclusions: Myasthenia gravis may be a complication of treatment of melanoma with dabrafenib and trametinib. The mechanism by which this occurs is unknown.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".