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Record W4399443863 · doi:10.1111/ijd.17236

Disparities and barriers to the access of biologics in moderate‐to‐severe adult psoriasis

2024· review· en· W4399443863 on OpenAlexaff
Vincent Wan, Alireza Habibi, Lorena Alexandra Mija, Parsa Abdi, Rishika Selvakumar, Ilya Mukovozov

Bibliographic record

VenueInternational Journal of Dermatology · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsUniversité de MontréalMemorial University of NewfoundlandUniversity of British Columbia
Fundersnot available
KeywordsMedicinePsychosocialMEDLINEPsoriasisSocioeconomic statusComorbidityHealth equityFamily medicineIntensive care medicineEnvironmental healthPopulationDermatologyPublic healthPsychiatryNursing

Abstract

fetched live from OpenAlex

Psoriasis is a chronic, relapsing inflammatory skin disorder that is associated with substantial physical and psychosocial comorbidity. Although biologic agents have offered transformative therapeutic advantages to those unresponsive to traditional treatments, data from recent literature indicate significant undertreatment of certain populations, highlighting potential barriers to access. This review aims to comprehensively elucidate barriers to biological therapy, addressing a recognized gap in the current literature. A search was conducted using MEDLINE, Embase, and Web of Science to investigate the obstacles and disparities that prevent access to biologic treatments in biologic-naïve psoriatic patients. Emergent themes were then systematically categorized into five primary domains: patient-level, prescriber-level, medicine-level, organizational-, and external environment-level factors. Our results demonstrate pronounced barriers and disparities encompassing increased age, race, socioeconomic status, rural location, cost and insurance, and insufficient knowledge that may hinder access to biologic treatments among psoriatic patients. Further research on how these barriers can be effectively addressed is needed to optimize treatment 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.334
Teacher spread0.300 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations16
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of DermatologySame topicPsoriasis: Treatment and PathogenesisFrench-language works237,207