Osimertinib tolerance in a patient with Stevens Johnson syndrome during osimertinib therapy after treatment with pembrolizumab
Bibliographic record
Abstract
BACKGROUND: Osimertinib has emerged as an important tool in the treatment of non-small cell lung cancers (NSCLC) with certain activating mutations of epidermal growth factor receptor (EGFR). However, Osimertinib may cause adverse effects, including severe cutaneous adverse reactions (SCARs) such as Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). The risk of certain adverse effects may be increased in the setting of recent use of immune checkpoint inhibitor (ICI) therapy, although it is unclear whether recent use of ICI therapy is a risk factor for Osimertinib-induced SJS specifically. CASE PRESENTATION: We present a patient with EGFR L858R mutation-positive metastatic NSCLC who developed Osimertinib-induced SJS after recent administration of eight cycles of a pembrolizumab-containing chemotherapy regimen. Osimertinib, which was the best treatment targeting his lung cancer, was avoided due to history of SJS. Four years later, because of unresponsiveness or side effects of alternative treatments, he underwent Osimertinib challenge and tolerated it. CONCLUSION: This case highlights the importance of multi-disciplinary care and supports the hypothesis that the risk of SJS to Osimertinib is significantly higher in the context of recent administration of ICI therapy and, patients may tolerate Osimertinib after certain time has elapsed after the last dose of ICI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| 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".