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Record W2323267995 · doi:10.3899/jrheum.130455

Dermatology Screening Tools: Project Update from the GRAPPA 2012 Annual Meeting

2013· article· en· W2323267995 on OpenAlexaffvenue
Dafna D. Gladman, Philip Helliwell, Majed Khraishi, Kristina Callis Duffin, Philip J. Mease

Bibliographic record

VenueThe Journal of Rheumatology · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsUniversity of TorontoUniversity Health NetworkMemorial University of Newfoundland
Fundersnot available
KeywordsMedicinePsoriatic arthritisPsoriasisCohortDermatologyCohort studyMedical physicsInternal medicine

Abstract

fetched live from OpenAlex

Several screening tools for early identification of psoriatic arthritis (PsA) have been developed. While these tools had high sensitivity and specificity during their development and initial validation, it remained to be determined how they would function with widespread use. At the 2012 annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) in Stockholm, Sweden, these tools were compared for their utility when used to screen patients for PsA in clinics other than those in which they were developed. The screening tools did not perform as well as previously published, and members suggested new tools may need to be developed. An additional study of the prevalence of PsA in a large cohort of psoriasis patients, the PREPARE study, which investigated the use of screening questionnaires, was also presented.

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)0.023
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.008
Science and technology studies0.0000.000
Scholarly communication0.0030.003
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.006

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 head0.022
GPT teacher head0.240
Teacher spread0.219 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2013
Admission routes2
Has abstractyes

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