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Record W4401280614

Spevigo® (Spesolimab-Sbzo) Injection for the Treatment of Generalized Pustular Psoriasis.

2024· article· en· W4401280614 on OpenAlexaff
Aditya K. Gupta, Avantika Mann, Kimberly Vincent, William Abramovits

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsMediprobe Research (Canada)University of Toronto
Fundersnot available
KeywordsGeneralized pustular psoriasisPustular psoriasisDermatologyMedicinePsoriasis
DOInot available

Abstract

fetched live from OpenAlex

(spesolimab-sbzo) injection was recently approved for the treatment of generalized pustular psoriasis (GPP) in adults aged 18- 75 years. Spesolimab, a monoclonal antibody, binds to the interleukin-36 (IL-36) receptor and prevents its activation by IL-36 cytokines, leading to reduced inflammation, skin lesions, and flares. In a randomized placebo-controlled, phase 2 study (Effisayil-1, NCT03782792), 53 patients were randomized to spesolimab (n = 35) and placebo (n = 18) to evaluate the effect of a one-time 900-mg dose of spesolimab versus placebo against GPP flares. The primary endpoint was Generalized Pustular Psoriasis Physician Global Assessment (GPPGA) pustulation subscore of 0 (no visible pustules) and the key secondary endpoint was the GPPGA total score of 0 or 1 (clear or almost clear skin) at the end of week 1. The primary endpoint was achieved by 54% (19/35) of patients in the spesolimab group and 6% (1/18) of patients in the placebo group. The key secondary endpoint was achieved by 43% (15/35) of patients in the spesolimab group and 11% (2/18) of patients in the placebo group. In the first week, adverse events (mild to severe) were reported in 66% (22/35) of patients in the spesolimab group and 56% (10/18) in the placebo group.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.033
GPT teacher head0.248
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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