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Record W2905794790 · doi:10.1007/s13555-018-0279-5

Clinical Goals and Barriers to Effective Psoriasis Care

2018· article· en· W2905794790 on OpenAlexafffund
Bruce Strober, Joelle M. van der Walt, April W. Armstrong, Marc Bourcier, André Vicente Esteves de Carvalho, Edgardo Chouela, Arnon D. Cohen, Charles N. Ellis, A.Y. Finlay, Alice B. Gottlieb, Jóhann E. Guðjónsson, Lars Iversen, C. Elise Kleyn, Craig L. Leonardi, Charles Lynde, Caitriona Ryan, Colin Theng, Fernando Valenzuela, Ronald Vender, Jashin J. Wu, Helen Young, Alexa B. Kimball

Bibliographic record

VenueDermatology and Therapy · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsUniversity of TorontoUniversité de SherbrookeDermatrials ResearchProbity Medical Research
FundersCilagAllerganMedacAstellas PharmaGlenmark PharmaceuticalsNational Psoriasis FoundationPfizerIncyteDermiraSun PharmaRegeneron PharmaceuticalsUniversity of ConnecticutEli Lilly and CompanyCardiff UniversityCelgeneLEO PharmaOrtho DermatologicsGilead SciencesSanofiGlaxoSmithKlineGaldermaBristol-Myers SquibbMeiji Seika PharmaCoherus BiosciencesUCB PharmaValeant Pharmaceuticals InternationalAmgen
KeywordsPsoriasisPaceMedicineDiseaseIntensive care medicineAlternative medicineBusinessFamily medicineDermatologyPathology

Abstract

fetched live from OpenAlex

Engaging global key opinion leaders, the International Psoriasis Council (IPC) held a day-long roundtable discussion with the primary purpose to discuss the treatment goals of psoriasis patients and worldwide barriers to optimal care. Setting clear expectations might ultimately encourage undertreated psoriasis patients to seek care in an era in which great gains in therapeutic efficacy have been achieved. Here, we discuss the option for early treatment of all categories of psoriasis to alleviate disease impact while emphasizing the need for more focused attention for psoriasis patients with mild and moderate forms of this autoimmune disease. In addition, we encourage policy changes to keep pace with the innovative therapies and clinical science and highlight the demand for greater understanding of treatment barriers in resource-poor countries.

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)0.050
metaresearch head score (Gemma)0.086
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.266

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.086
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0120.006
Open science0.0020.010
Research integrity0.0060.013
Insufficient payload (model declined to judge)0.0070.002

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 head0.014
GPT teacher head0.292
Teacher spread0.279 · 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

Citations93
Published2018
Admission routes2
Has abstractyes

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