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Record W2054573088 · doi:10.3899/jrheum.090822

Cardiovascular Disease and Risk Factors in Patients with Psoriasis and Psoriatic Arthritis

2010· review· en· W2054573088 on OpenAlexvenueno aff
Anne‐Marie Tobin, Douglas J. Veale, Oliver FitzGerald, Sarah Rogers, Paul M. Collins, Donal O’Shea, Brian Kirby

Bibliographic record

VenueThe Journal of Rheumatology · 2010
Typereview
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsoriasisPsoriatic arthritisDiseasePopulationMetabolic syndromeRisk factorInternal medicineIncidence (geometry)EpidemiologyDermatologyObesityEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: Patients with psoriasis and psoriatic arthritis (PsA) have an increased incidence of cardiovascular disease (CVD) and cardiovascular risk factors such as smoking, hypertension, and metabolic syndrome compared to the normal population. Patients with psoriasis and PsA may also have increased risk from nonconventional risk factors such as raised levels of homocysteine and excessive alcohol consumption. We conducted a comprehensive review of the literature on CVD and all cardiovascular risk factors in patients with psoriasis and PsA. DATA SOURCES: All studies identified from a Medline (www.ncbi.nlm.nih.gov) search pertaining to CVD, individual risk factors in psoriasis, and PsA were included. STUDY SELECTION: Studies included a healthy reference population, were published between 1975 and 2009, and were written in English. RESULTS: Our search yielded 14 studies that documented rates of CVD in patients with psoriasis and PsA compared to controls. Substantial evidence points to elevated risk of CVD in patients with psoriasis and PsA. CONCLUSION: It remains difficult to conclude if risk factors are caused by psoriasis or share a common pathogenesis. Physicians treating patients with psoriasis and PsA must be aware of all potential cardiovascular risk factors in their patients.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.844
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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 designOther design
Domainnot available
GenreReview

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

Citations136
Published2010
Admission routes1
Has abstractyes

Explore more

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