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Record W2093738326 · doi:10.3899/jrheum.101119

Psoriasis Epidemiology Screening Tool (PEST): A Report from the GRAPPA 2009 Annual Meeting

2011· article· en· W2093738326 on OpenAlexvenueaboutno aff
Philip Helliwell

Bibliographic record

VenueThe Journal of Rheumatology · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsoriatic arthritisPsoriasisEpidemiologyPopulationDermatologyArthritisPhysical therapyFamily medicineInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Patients with psoriasis attending general practitioner and dermatology clinics may complain about their joints, but it may be difficult for the nonrheumatologist to distinguish psoriatic arthritis (PsA) from other forms of arthritis. A screening tool for PsA would therefore be useful to both general practitioners and dermatologists and help identify patients for further evaluation by a rheumatologist. Although several screening tools have been developed, the Psoriasis Epidemiology Screening Tool (PEST) has the advantage of simplicity and ease of use. This new instrument consists of 5 simple questions supported by the addition of a manikin for patient markup. During development, the questionnaire has shown a sensitivity of 0.94 and a specificity of 0.78. Further validation of this and the other questionnaires is now required. A "head to head" study of the PEST, ToPAS (Toronto Psoriatic Arthritis Screening questionnaire), and PASE (Psoriatic Arthritis Screening and Evaluation) tools is planned in a secondary-care population with psoriasis. This study is important not only to confirm the comparative performance of the instruments, but also to confirm the high figures for sensitivity in a secondary-care population.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.150
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.259
Teacher spread0.215 · 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

Citations43
Published2011
Admission routes2
Has abstractyes

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