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Record W2037862910 · doi:10.3899/jrheum.120237

Metabolic Syndrome in Patients with Psoriatic Disease

2012· article· en· W2037862910 on OpenAlexvenueno aff
Joel M. Gelfand, Howa Yeung

Bibliographic record

VenueJournal of Rheumatology Supplement · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Heart, Lung, and Blood InstituteUniversity of PennsylvaniaGenentechCelgeneNational Institutes of HealthPfizerAmgen
KeywordsMedicineMetabolic syndromePsoriasisDyslipidemiaDiseaseInsulin resistanceObesityInternal medicineDiabetes mellitusIntensive care medicineDermatologyEndocrinology

Abstract

fetched live from OpenAlex

Psoriasis is a common Th-1 and Th-17-mediated chronic inflammatory disease that has been associated with metabolic syndrome, a constellation of cardiovascular risk factors including obesity, hypertension, dyslipidemia, and insulin resistance. Overlapping inflammatory pathways and genetic susceptibility may be potential biologic links underlying this association. Multiple epidemiologic studies have consistently demonstrated higher prevalence of metabolic syndrome in patients with psoriasis. Dose-response relationships between more severe psoriasis and higher prevalence of metabolic syndrome components were recently established. This association has important clinical implications for the comprehensive management of psoriasis: Patients with psoriasis should be routinely screened for metabolic syndrome and treated accordingly to manage cardiometabolic risk, while clinicians should monitor potential effects on treatment efficacy and safety in patients with comorbid psoriasis and metabolic syndrome. Further research will be necessary to establish the directionality of this association and to explore the effect of treatment on these comorbid diseases.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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.

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

Citations109
Published2012
Admission routes1
Has abstractyes

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