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Record W4385456255 · doi:10.3899/jrheum.2023-0525

Diversity, Equity, and Inclusion: Sex and Gender and Intersectionality With Race and Ethnicity in Psoriatic Disease

2023· article· en· W4385456255 on OpenAlexaffvenue
Lihi Eder, Alaina J. James, Irene van der Horst‐Bruinsma, Laura C. Coates, Niti Goel

Bibliographic record

VenueThe Journal of Rheumatology · 2023
Typearticle
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsWomen's College Hospital
FundersMedacNational Institute for Health and Care ResearchCelgeneBiogenGilead SciencesAmgenPfizerEli Lilly and Company
KeywordsPsoriatic arthritisMedicineIntersectionalityEthnic groupPsoriasisDiseaseRace (biology)Inclusion (mineral)Health equityDiversity (politics)GerontologyPublic healthGender studiesInternal medicineDermatologyPathologySociology

Abstract

fetched live from OpenAlex

Sex (biological attributes associated with being male or female) and gender (sociocultural-driven traits and behaviors related to being a man or a woman) are emerging as important determinants of disease course and response to therapy in patients with psoriasis and psoriatic arthritis (PsA). Although psoriatic disease (PsD) is equally prevalent in men and women, the condition affects them in different and unique ways, giving rise to sex- and gender-related differences in clinical presentation, including baseline disease activity, disease course, and response to treatment. Better understanding of the roles sex and gender play in the development and evolution of PsD has the potential to improve patient care. The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) continues its effort to highlight issues related to diversity, equity, and inclusion in people with PsD by dedicating a session during the annual meeting to sex and gender and their intersectionality with race and ethnicity in individuals with PsA.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0040.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.355
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2023
Admission routes2
Has abstractyes

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