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Record W4253404384 · doi:10.3899/jrheum.190124

Report of the Skin Research Workgroups from the GRAPPA 2018 Annual Meeting

2019· article· en· W4253404384 on OpenAlexvenueno aff
Lourdes M. Pérez-Chada, Joseph F. Merola, April W. Armstrong, Alice B. Gottlieb

Bibliographic record

VenueThe Journal of Rheumatology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsnot available
Fundersnot available
KeywordsPsoriasisMedicinePsoriatic arthritisDelphi methodClinical trialDermatologyQuality of life (healthcare)DelphiFamily medicinePhysical therapyInternal medicineNursing

Abstract

fetched live from OpenAlex

At the 2018 annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA), the International Dermatology Outcome Measures (IDEOM) Psoriasis Working Group presented an overview of its efforts to enhance clinical care and research in both the clinical setting as well as in clinical trials for psoriasis. First, the group discussed the results of a Delphi survey conducted in collaboration with the American Academy of Dermatology to agree on a unique physician-reported global assessment to measure the quality of care delivered to patients with psoriasis and other chronic inflammatory dermatoses. Second, the group summarized its efforts to select outcome measures for "PsA symptoms" and "treatment satisfaction," 2 of the domains of the psoriasis core domain set that were established by IDEOM. Finally, the Psoriasis and Psoriatic Arthritis Clinics Multicenter Advancement Network (PPACMAN) presented an update on its clinical, educational, and research missions to foster the development of combined clinics for psoriatic disease, increase disease awareness, and accelerate management.

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)0.031
metaresearch head score (Gemma)0.022
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 score0.031
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0050.001
Scholarly communication0.0060.002
Open science0.0020.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0260.010

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 head0.024
GPT teacher head0.275
Teacher spread0.251 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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