Cases from the irAE Tumor Board: A Multidisciplinary Approach to a Patient Treated with Immune Checkpoint Blockade Who Presented with a New Rash
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) have revolutionized the treatment paradigms for a broad spectrum of malignancies. Because immune checkpoint inhibitors rely on immune reactivation to eliminate cancer cells, they can also lead to the loss of immune tolerance and result in a wide range of phenomena called immune-related adverse events (irAEs). At our institution, the management of irAEs is based on multidisciplinary input obtained at an irAE tumor board that facilitates expedited opinions from various specialties and allows for a more uniform approach to these patients. In this article, we describe a case of a patient with metastatic urothelial carcinoma who developed a maculopapular rash while being treated with a programmed death-ligand 1 inhibitor. We then describe the approach to management of dermatologic toxicities with ICIs based on the discussion at our irAE Tumor Board. KEY POINTS: Innocuous symptoms such as pruritis or a maculopapular rash may herald potentially fatal severe cutaneous adverse reactions (SCARs); therefore, close attention must be paid to the symptoms, history, and physical examination of all patients.Consultation with dermatology should be sought for patients with grade 3 or 4 toxicity or SCARs and prior to resumption of immune checkpoint inhibitors for patients with grade 3 or higher toxicity.A multidisciplinary immune-related adverse events (irAE) tumor board can facilitate timely input and expertise from various specialties, thereby ensuring a streamlined approach to management of irAEs.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| 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".