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Record W3000494588 · doi:10.5152/eurjrheum.2019.19126

Psoriasis Symptom Inventory (PSI) as a patient-reported outcome in mild psoriasis: Real life data from a large psoriatic arthritis registry

2020· article· en· W3000494588 on OpenAlexaff
Sibel Zehra Aydın, Gezmiş Kimyon, Cem Özişler, Emine Figen Tarhan, Esen Kasapoğlu Günal, Adem Küçük, Ahmet Omma, Dilek Solmaz, Emine Duygu Ersözlü, Fatih Yıldız, Müge Aydın Tufan, Muhammet Çınar, Rıdvan Mercan, Şule Yavuz, Fatıma Arslan Alhussain, Abdülsamet Erden, Meryem Can, Gözde Yıldırım Çetin, Levent Kılıç, Sibel Bakırcı, Noura Al Osaimi, Umut Kalyoncu

Bibliographic record

VenueEuropean Journal of Rheumatology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsPsoriasisMedicinePsoriatic arthritisPatient-reported outcomeDermatologyQuality of life (healthcare)

Abstract

fetched live from OpenAlex

OBJECTIVE: Our aim is to test the validity of the Psoriasis Symptom Inventory (PSI), a patient-reported outcome, to assess the psoriasis severity within the scope of rheumatology. METHODS: Within the PsA international database (PSART-ID), 571 patients had PSI, while 322 of these also showed body surface area (BSA). Correlations between PSI, BSA, and other patient- and physician-reported outcomes were investigated. RESULTS: There was a good correlation between PSI and BSA (r=0.546, p<0.001), which was even higher for mild psoriasis (BSA<3 (n=164): r=0.608, p<0.001). PSI significantly correlated with fatigue, pain, and patient and physician global parameters (p<0.001). CONCLUSION: PSI has a good correlation with other patient- and physician-reported outcomes, and our findings support its use in rheumatology practice.

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)0.003
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.052
GPT teacher head0.263
Teacher spread0.211 · 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
Published2020
Admission routes1
Has abstractyes

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Same venueEuropean Journal of RheumatologySame topicPsoriasis: Treatment and PathogenesisFrench-language works237,207