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Record W4224264528 · doi:10.3390/curroncol29040234

Cutaneous Adverse Events Associated with Immune Checkpoint Inhibitors: A Review Article

2022· review· en· W4224264528 on OpenAlexvenueno aff
Chieh‐Hsun Chen, Hsin‐Su Yu, Sebastian Yu

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
FundersKaohsiung Medical University Chung-Ho Memorial HospitalMinistry of Science and Technology, Taiwan
KeywordsMedicineMaculopapular rashAdverse effectRashImmunologyImmune systemImmunotherapyCancerMonoclonal antibodyDermatologyAntibodyInternal medicine

Abstract

fetched live from OpenAlex

Immune checkpoint inhibitors (ICIs) have emerged as novel options that are effective in treating various cancers. They are monoclonal antibodies that target cytotoxic T-lymphocyte antigen 4 (CTLA-4), programmed cell death 1 (PD-1), and programmed cell death-ligand 1 (PD-L1). However, activation of the immune systems through ICIs may concomitantly trigger a constellation of immunologic symptoms and signs, termed immune-related adverse events (irAEs), with the skin being the most commonly involved organ. The dermatologic toxicities are observed in nearly half of the patients treated with ICIs, mainly in the form of maculopapular rash and pruritus. In the majority of cases, these cutaneous irAEs are self-limiting and manageable, and continuation of the ICIs is possible. This review provides an overview of variable ICI-mediated dermatologic reactions and describes the clinical and histopathologic presentation. Early and accurate diagnosis, recognition of severe toxicities, and appropriate management are key goals to achieve the most favorable outcomes and quality of life in cancer patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.112
GPT teacher head0.414
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations53
Published2022
Admission routes1
Has abstractyes

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