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Record W3033338019 · doi:10.1177/1203475420932514

Long-Term Single Center Experience in Treating Plaque Psoriasis With Guselkumab

2020· article· en· W3033338019 on OpenAlexaffabout
Khalad Maliyar, Ashley O’Toole, Melinda Gooderham

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsSKiN HealthUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsMedicinePsoriasisDermatologyAdverse effectClinical trialPopulationMedical recordHeadachesSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Clinical trial data have shown guselkumab, an interleukin-23 inhibitor, to be efficacious and safe for the treatment of psoriasis. However, there is very little real-world experience using guselkumab in the community setting that has been documented. OBJECTIVES: The goal of this study was to determine real-life outcomes of guselkumab use in patients with moderate-to-severe psoriasis in a community dermatology practice. METHODS: A retrospective chart review of electronic medical records was conducted in patients with moderate-to-severe psoriasis who were prescribed guselkumab at a community dermatology office in Ontario, Canada. RESULTS: Of the 89 patients who received at least 1 dose of guselkumab, 79 had follow-up information at the time of review, with 71 patients receiving ongoing treatment. In our cohort of patients, 73.3% achieved clinically significant clearance of psoriasis with a global assessment of clear or almost clear defined as a body surface area involvement of <1%. Guselkumab was generally well tolerated and caused no serious adverse events. The most common reported side effects were nasopharyngitis, headaches, upper respiratory tract infections, gastrointestinal upset, and arthralgia. CONCLUSION: Overall, guselkumab was a safe and well-tolerated treatment with significant clinical improvement in our patient population.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.257
Teacher spread0.216 · 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.

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

Citations36
Published2020
Admission routes2
Has abstractyes

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