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Record W4389439162 · doi:10.2147/itt.s377076

Managing Cardiovascular Risk in Systemic Lupus Erythematosus: Considerations for the Clinician

2023· review· en· W4389439162 on OpenAlexaff
Teresa Semalulu, Achieng Tago, Kevin Zhao, Konstantinos Tselios

Bibliographic record

VenueImmunoTargets and Therapy · 2023
Typereview
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineDiseaseDyslipidemiaCoronary artery diseaseEndothelial dysfunctionArterial stiffnessPopulationIntensive care medicineCardiologyHydroxychloroquineRisk assessmentInternal medicineAtherosclerotic cardiovascular diseaseBlood pressure

Abstract

fetched live from OpenAlex

A significant improvement in the survival of patients with systemic lupus erythematosus (SLE) over recent decades is largely attributed to the impact of disease-modifying therapies on end-organ damage. Thus, cardiovascular disease now represents the leading cause of mortality in SLE. Various disease-specific mechanisms are responsible for advanced atherosclerosis, as they lead to premature endothelial dysfunction, arterial stiffness, arterial wall thickening, and plaque formation. Consequently, in the assessment of cardiovascular risk in SLE, we must not only consider traditional risk factors (ie, age, gender, dyslipidemia) but also the additional role of non-traditional risk factors such as persistent disease activity and prolonged corticosteroid use. Cardiovascular risk assessment incorporates general cardiovascular screening, as existing risk prediction scores underestimate cardiovascular risk in this patient population. There is also an expanding role of imaging modalities in screening. Risk reduction strategies integrate unique considerations for the use of low-dose aspirin and more stringent hypertension targets. Hydroxychloroquine is the only disease-modifying therapy with known cardiovascular benefit in SLE, though this is a promising area of study.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
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 head0.101
GPT teacher head0.370
Teacher spread0.269 · 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 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

Citations20
Published2023
Admission routes1
Has abstractyes

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