Desensitization Protocol for Cemiplimab-Related Infusion Reaction in Cutaneous Squamous Cell Carcinoma: A Case Report and Literature Review
Bibliographic record
Abstract
BACKGROUND: The landscape of systemic therapies for advanced non-melanoma skin cancers has been revolutionized by the advent of immunotherapy. Cemiplimab is the only immune checkpoint inhibitor (ICI) approved by the European Medicine Agency for recurrent/metastatic cutaneous squamous cell carcinoma (cSCC). Its excellent efficacy outcomes are achieved due to its good tolerability profile. The drug-related hypersensitivity reaction (HSR) is a well-known issue in oncology, but it is rarely reported in respect to immune checkpoint inhibitors. Cemiplimab is among the agents with the best infusion tolerability profiles. Clinical practice guidelines in this field are lacking. RESULTS: We report on the successful management of a severe infusion reaction induced by Cemiplimab in a patient with cSCC based on a desensitization protocol, which led to adequate treatment delivery and prolonged clinical benefit. A review of the available literature on HSR rates and its management with ICIs, and on drug desensitization (DD) protocols and their efficacy, was conducted to highlight the limited knowledge on this topic and its importance. CONCLUSION: Our experience highlights the need for a DD protocol in order to improve the treatment of HSRs, particularly when elicited by an immunotherapy agent, preventing treatment discontinuation and preserving its efficacy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".