Systemic treatment of immune checkpoint inhibitor‐induced psoriasis: Inference‐based guidance
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICIs) are increasingly used to treat various cancers. Their use may result in immune-related adverse events, including psoriasis. When managing psoriasis, induced or exacerbated by an ICI, there are concerns regarding immunosuppression from systemic agents for the treatment of psoriasis (saPs) and the potential impact on ICI efficacy. No direct, high-level evidence exists to address these concerns. OBJECTIVE: To address clinically relevant questions regarding the management of ICI-mediated psoriasis (ICI-Ps) with saPs. METHODS: We convened a multidisciplinary panel of 15 international specialists in dermatology, oncology, immunology, and rheumatology. A Delphi process defined clinical concerns related to the systemic treatment of ICI-Ps, focusing on the potential of saPs to impact ICI effectiveness. The saPs considered included biologics targeting tumour necrosis factor, interleukin (IL)-17, IL-12/23 and IL-23, traditional systemic therapies (cyclosporine, methotrexate), small molecules targeting phosphodiesterase-4 or tyrosine kinase 2, systemic retinoids (acitretin), and systemic corticosteroids. A systematic review of the literature was supplemented with evidence supporting an inference-based methodology to derive conclusions on the use of systemic therapies in patients with ICI-Ps. The specialist panel rated the strength of the conclusions using a probabilistic scale. RESULTS: After reviewing the totality of direct and indirect evidence, we drafted inference-based conclusions and ascribed a level of support, focusing on the potential impact of saPs on ICI efficacy. This work provides a structured framework informing healthcare professional and patient discussions on the risks and benefits of using saPs in patients with cancer who experience ICI-Ps. CONCLUSIONS: Although there is no direct evidence, we support the following conclusions: saPs may be used to treat ICI-Ps without an appreciable loss of ICI effectiveness. Generally, it is not necessary to interrupt ICI therapy. When available, non-steroid saPs are preferred over systemic corticosteroids for the treatment of psoriasis.
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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 | 0.068 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".