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Record W4213031527 · doi:10.1007/s13555-022-00690-5

Cumulative Clinical Benefits of Biologics in the Treatment of Patients with Moderate-to-Severe Psoriasis over 1 Year: a Network Meta-Analysis

2022· article· en· W4213031527 on OpenAlexaff
Andrew Blauvelt, Melinda Gooderham, C.E.M. Griffiths, April W. Armstrong, Baojin Zhu, Russel Burge, Gaia Gallo, Jiaying Guo, Alyssa Garrelts, Mark Lebwohl

Bibliographic record

VenueDermatology and Therapy · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsSKiN Health
FundersManchester Biomedical Research CentreNational Institute for Health and Care ResearchDermiraCastle BiosciencesGaldermaBristol-Myers SquibbEli Lilly and CompanyOrtho DermatologicsSanofi GenzymeIncyteUCB PharmaSun PharmaRegeneron PharmaceuticalsCelgeneSanofiAmgenPfizer
KeywordsPsoriasis Area and Severity IndexMedicinePsoriasisInternal medicinePlaceboClinical trialRandomized controlled trialIxekizumabGastroenterologyDermatology

Abstract

fetched live from OpenAlex

INTRODUCTION: Both early clinical improvement and long-term maintenance of clinical efficacy of treatments matter to patients with psoriasis. We compared cumulative clinical benefits of treatment with biologics over 1 year based on the area under the curve (AUC) for Psoriasis Area and Severity Index (PASI) 100 and PASI 90 responses in patients with moderate-to-severe psoriasis using a network meta-analysis (NMA). METHODS: Published phase 3 randomized, placebo- or active-controlled clinical trial data for biologics approved for the treatment of moderate-to-severe psoriasis were obtained from a systematic literature review up to 30 September 2020. Eighteen clinical trials that included data from baseline to 48 or 52 weeks where AUC could be calculated were included. Data were compared using a fixed-effect model with a separate random-effect baseline model to account for effects of the placebo arm. Cumulative clinical benefit was estimated using the AUC for PASI 100 and PASI 90 responses (complete and almost-complete skin clearance, respectively). Normalized AUC was compared using Bayesian NMA. Cumulative days of response were calculated using normalized AUC and study duration. RESULTS: Interleukin (IL)-17 and IL-23 inhibitors demonstrated greater cumulative clinical benefits for both PASI 100 and PASI 90 versus IL-12/23 and tumor necrosis factor inhibitors. Over 52 weeks, cumulative days with PASI 100 were greatest with ixekizumab [158.7 (95% credible interval, 147.4, 170.0) days] followed by risankizumab [154.0 (144.9, 163.4) days]; PASI 90 days were greatest with risankizumab [249.3 (239.5, 259.2) days] followed by ixekizumab [238.8 (227.1, 250.8) days]. Both ixekizumab and risankizumab showed greater cumulative days with PASI 100 or PASI 90 responses versus secukinumab [117.9 (110.7, 125.2) and 215.5 (208.2, 223.1) days, respectively] and greater cumulative days with PASI 100 versus guselkumab [130.7 (120.5, 140.9) days]. CONCLUSION: For complete and almost-complete skin clearance, ixekizumab and risankizumab provided the greatest cumulative clinical benefits over 1 year.

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)0.032
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.043
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0130.051
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.302
Teacher spread0.229 · 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 designMeta-analysis
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

Citations20
Published2022
Admission routes1
Has abstractyes

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