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Record W4414973762 · doi:10.1007/s13555-025-01535-7

Considerations for Treating Generalized Pustular Psoriasis (GPP): A Narrative Review

2025· review· en· W4414973762 on OpenAlexaff
Charles Lynde, Vimal H. Prajapati, Melinda Gooderham, Chih-ho Hong, Mark G. Kirchhof, Perla Lansang, Julien Ringuet, Irina Turchin, Ronald Vender, Jensen Yeung, Kim Papp

Bibliographic record

VenueDermatology and Therapy · 2025
Typereview
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsDalhousie UniversityCentre de Recherche Dermatologique du Québec MétropolitainCentre de Recherche Industrielle du QuébecWomen's College HospitalHospital for Sick ChildrenDermatrials ResearchHealth Sciences CentreOttawa HospitalMcGill UniversityUniversity of OttawaSunnybrook Health Science CentreQuest University CanadaQueen's UniversityProbity Medical ResearchUniversity of British ColumbiaMcMaster UniversitySKiN HealthUniversity of CalgaryLynde Centre for DermatologyUniversity of Toronto
FundersBoehringer Ingelheim
KeywordsGeneralized pustular psoriasisPsoriasisNarrative reviewBiologic AgentsPlaque psoriasisPathogenesis

Abstract

fetched live from OpenAlex

Generalized pustular psoriasis (GPP) is a rare, chronic dermatological condition characterized by widespread pustulation that may be associated with other cutaneous and systemic manifestations. If left untreated, it may be life-threatening. Therapies developed for plaque psoriasis have been used to treat GPP with limited efficacy; however, these therapies do not target the interleukin (IL)-36 pathway, which is the most common pathway implicated in the pathogenesis of GPP. A systemic biologic targeting the IL-36 receptor for the treatment of GPP is currently the only approved treatment for GPP and allows for an opportunity to improve patient outcomes. This manuscript outlines practical considerations that aim to provide guidance on personalizing the treatment of GPP.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.336
Teacher spread0.287 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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