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Record W4408498282 · doi:10.25251/skin.10.supp.531

Long-Term Effectiveness of Guselkumab vs. Other Biologic Therapies Among Plaque Psoriasis Patients in the CorEvitas Psoriasis Registry

2025· article· en· W4408498282 on OpenAlexaboutno aff
Bruce Strober, April W. Armstrong, Timothy Fitzgerald, Katelyn Rowland, Olivia Choi, Daphne Chan, Alvin Li, Adam Šíma, Thomas Eckmann, Sandra I. Main, Mark Lebwohl

Bibliographic record

VenueSKIN The Journal of Cutaneous Medicine · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsnot available
Fundersnot available
KeywordsPsoriasisPlaque psoriasisMedicineDermatologyTerm (time)

Abstract

fetched live from OpenAlex

Introduction: The efficacy and real-world treatment persistence of guselkumab (GUS) has been shown to be superior to other advanced treatment options [1,2] for psoriasis (PsO); however, long-term real-world effectiveness vs. other biologic therapies warrants further study. Therefore, this study compared the real-world effectiveness of GUS with adalimumab (ADA), ixekizumab (IXE), secukinumab (SEC), and ustekinumab (UST) in PsO patients through 30 months of follow-up. Methods: An active comparator, new-user design was used to compare initiators of GUS to each comparator separately among adult patients with plaque PsO, an Investigator’s Global Assessment (IGA) score ≥2, and at least 30 months of follow-up prior to the data cutoff (June 2024) enrolled in the CorEvitas Psoriasis Registry in the US and Canada. Stabilized standardized mortality ratio (SMR) weights were used to balance baseline characteristics between GUS and each comparator. The primary outcome was achievement of IGA 0/1 at 30 months. Dermatology Life Quality Index (DLQI) 0/1 was assessed as a secondary outcome. Absolute differences were reported after incorporating the SMR weights. For all comparisons, P<0.05 was considered statistically significant and Bonferroni-Holm adjustments were made for multiple testing. Results: After balancing on baseline characteristics, significantly greater proportions of GUS initiators achieved IGA 0/1 vs. those in each comparator group (GUS 50.7% (n=428) vs. ADA 24.0% (n=309): difference=26.7%, 95% CI: 13.6-39.7; p<0.001; GUS 43.4% (n=587) vs. IXE 33.6% (n=580): difference=9.9%, 95% CI: 2.6-17.1; p=0.005; GUS 45.8% (n=546) vs. SEC 29.9% (n=614): difference=15.9%, 95% CI: 6.8-25.0; p<0.001; GUS 45.9% (n=466) vs. UST 36.2% (n=224): difference=9.7%, 95% CI: 0.6-18.7; p=0.036). Similarly, greater proportions of GUS initiators achieved DLQI 0/1 vs. the comparators. Conclusion: Our findings suggest that GUS was superior to ADA, IXE, SEC, and UST in achieving both IGA 0/1 and DLQI 0/1 at 30 months. This is among the largest and longest real-world comparative effectiveness studies of GUS vs. ADA, IXE, SEC, and UST in PsO patients. Additional studies with longer follow-up and inclusion of emerging therapies could provide further data to inform clinical treatment considerations.

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)0.011
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2025
Admission routes1
Has abstractyes

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