Eruptive Keratoacanthoma-Type Squamous Cell Carcinomas in Patients Taking Sorafenib for the Treatment of Solid Tumors
Bibliographic record
Abstract
BACKGROUND: Protein kinases (PKs) are indispensable for most cellular processes, and deregulation of PKs can lead to activation of oncogenic and anti-apoptotic pathways and immune dysregulation. OBJECTIVE: To report the development of keratoacanthoma (KA)-type squamous cell carcinomas (SCCs) in patients treated with the multikinase inhibitor sorafenib for the treatment of solid tumors, to present the possible mechanisms for induction of these SCCs, and to discuss the implications for discontinuation of therapy and possible cotherapies to decrease this side effect. PARTICIPANTS: Fifteen patients taking the multikinase inhibitor sorafenib for the treatment of solid tumors who developed multiple KA-type SCCs, which continued to develop while the patients were undergoing therapy but stopped with discontinuation of sorafenib. LIMITATIONS: This report is limited because it is a retrospective study that included only patients who developed multiple KA-type SCCs. CONCLUSIONS: Development of cutaneous SCCs appears to be a side effect limited to sorafenib, a multikinase inhibitor that inhibits not only multiple tyrosine kinases (TKs), but also the serine-threonine kinase Raf. The incidence of cutaneous SCCs does not appear greater with multikinase inhibitors that inhibit only TKs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".