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Record W2790907408 · doi:10.1177/247553031700200206

Greater Efficacy with Secukinumab Treatment is Associated with Greater Psoriasis Symptom Relief: Results from Secukinumab Clinical Trial Data

2017· article· en· W2790907408 on OpenAlexaff
Alice B. Gottlieb, Bruce Strober, Mark Lebwohl, Roland Kaufmann, David M. Pariser, Redzinaldas Narbutas, Judit Nyirady, Yang Zhao, Vivian Herrera, Lori McLeod, Dawn Odom, Boni E. Elewski

Bibliographic record

VenueJournal of Psoriasis and Psoriatic Arthritis · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsProbity Medical Research
Fundersnot available
KeywordsSecukinumabMedicinePsoriasisPsoriasis Area and Severity IndexItchingClinical trialInternal medicineDermatologySeverity of illnessPhysical therapyPsoriatic arthritis

Abstract

fetched live from OpenAlex

Background Psoriasis negatively affects patients’ quality of life. Secukinumab is a human interleukin-17A antagonist indicated for the treatment of moderate-to-severe plaque psoriasis. Objectives The current analysis evaluated the benefits of secukinumab by assessing relationships between disease severity and patient-reported symptoms. Methods Correlations between psoriasis-related itching, pain, and scaling and disease severity scores (Psoriasis Area and Severity Index [PASI] and Investigator's Global Assessment [IGA]) were evaluated at baseline, Week 12, and change from baseline to Week 12 using secukinumab clinical data from ERASURE and FIXTURE. Symptom responder status and PASI/IGA change were evaluated using logistic modeling. Results Correlation coefficients ranged 0.11-0.49 for PASI and 0.19-0.52 for IGA. Greater PASI response was related to greater symptom response/complete relief. Conclusions Results further demonstrate the relationship between traditional clinical measures of disease severity and patient-reported, psoriasis-related itching, pain, and scaling –- hence the need to consider both outcomes together to evaluate treatment effects in this disease fully.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.070
GPT teacher head0.308
Teacher spread0.238 · 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 designRandomized trial
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

Citations1
Published2017
Admission routes1
Has abstractyes

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