Acute Onset of a Life-Threatening Skin Toxicity Due to Osimertinib: Severe Psoriasis Versus Toxic Epidermal Necrolysis
Bibliographic record
Abstract
Osimertinib is a third-generation irreversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor currently used as first-line systemic therapy for advanced EGFR mutant non-small cell lung cancer. Osimertinib is generally very well tolerated with only a 1% risk of grade 3-4 skin toxicity. Here we present a case of a 68-year-old Asian male with advanced EGFR exon 19 deletion non-small cell lung cancer. After initiation of osimertinib 80 mg daily, he had a rapid worsening of his pre-existing scaly psoriatic plaques with desquamation. Treatment was withheld while psoriasis therapy was administered. He was rechallenged on osimertinib 40 mg daily and within three days developed fever, tachycardia and widespread skin desquamation. There was an initial concern of toxic epidermal necrolysis; however, this was ultimately determined to be a severe flare of psoriasis. This case serves as a reminder that severe and potentially life-threatening complications can occur, and it is imperative to maintain a high level of vigilance for unusual toxicities of EGFR tyrosine kinase inhibitors, including Stevens-Johnson Syndrome (SJS) and toxic epidermal necrolysis or psoriasis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".