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Record W3007535309 · doi:10.1080/09546634.2020.1729335

Long-term topical management of psoriasis: the road ahead

2020· article· en· W3007535309 on OpenAlexaff
Siegfried Segaert, Piergiacomo Calzavara‐Pinton, P. de la Cueva, Ahmad Jalili, D. Lons Danic, Andrew Pink, Diamant Thaçi, Melinda Gooderham

Bibliographic record

VenueJournal of Dermatological Treatment · 2020
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsProbity Medical ResearchQueen's University
FundersLEO Pharma Research Foundation
KeywordsMedicinePsoriasisContext (archaeology)TerminologyIntensive care medicineExpert opinionDermatology

Abstract

fetched live from OpenAlex

Topical therapies have been available for the treatment of psoriasis for several decades. Despite this and the availability of several types of topicals, with varying potency, and numerous vehicles of administration, the majority of clinical data and guidance is on short-term use in the management of psoriasis. The aim of this manuscript is to review the unmet needs that exist in the long-term management of psoriasis and provide the dermatology community with an understanding that a treatment regimen with topical therapies could be the best treatment option at least for some phases of this chronic relapsing disease. We present a 'call to action' on the need for clinical alignment on terminology in the field and recommend the term 'long-term management' be adopted as the most appropriate in the context of this manuscript. This expert opinion report provides a detailed review of the limited evidence available regarding long-term use of topical therapies for the management of psoriasis, alongside our key considerations and recommendations to assist dermatologists with the implementation of topicals as part of long-term management strategies. Long-term management should be considered mandatory to ensure patients receive appropriate proactive treatment which may help optimize adherence and long-term outcomes.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.276

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 head0.051
GPT teacher head0.318
Teacher spread0.267 · 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

Citations63
Published2020
Admission routes1
Has abstractyes

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