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THE STUDY OF CARDIOVASCULAR RISK ATTAINMENT AMONG PATIENTS WITH LUPUS AND RHEUMATOID ARTHRITIS

2025· article· en· W4410513268 on OpenAlexvenueno aff
A Mirabella, Richard Feinn, Mei Dong

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatoid arthritisSystemic lupus erythematosusInternal medicineLupus erythematosusArthritisPhysical therapyImmunologyDiseaseAntibody

Abstract

fetched live from OpenAlex

PV220 / #517 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose It has been well studied that patients with rheumatologic disease have an increased risk for cardiovascular (CV) events and those who are underserved are at especially high risk. Rheumatoid arthritis (RA and lupus (SLE) are the most common rheumatologic conditions. Therefore, goal of this project is to identify the prevalence of cardiovascular risk factors in patients with SLE or RA within the Yale rheumatology and to evaluate the success of current efforts to minimize CV risk. Methods In this 1 center, cross-sectional study, cardiovascular risk factor data, which include hyperlipidemia (HLD), hypertension (HTN), smoking, obesity, and diabetes (DM), were extracted from Yale health system from 2010-2023 with related ICD codes to investigate the prevalence and target attainment of traditional risk factors in patients with SLE and RA. A survey of knowledge gap of managing CV risk factors in rheumatological diseases was performed in Yale primary care residents who are primary providers serving the underserved patient population including those with rheumatological diseases. Results Cardiovascular disease (CVD) in this study includes coronary artery disease, cerebral vascular disease and peripheral vascular disease. Of total 295 patients with SLE, 138 (46.8%) had CVD and 157 (53.2%) had no CVD. Of those SLE with CVD vs. without, we found HLD (69.9% vs. 47.8%), HTN (76.8% vs.56.1%), smoking (52.9% vs. 41.1%), obesity (64.2% vs. 24.2%) and DM (20% vs. 14.2%). Of total 1680 patients with RA, 607 (36.1%) had CVD and 1073 (63.9%) had no CVD. Of those RA with CVD vs. without, we found HLD (88.8% vs. 56.6%), HTN (82.3% vs.54.9%), smoking (17.1% vs. 11.2%), obesity (51.9% vs. 45.2%) and DM (48.6% vs. 25.1%). 55 patients with SLE and 32 patients with RA were assessed the attainment of traditional risk factors for CVD. We found there were 68.1% with LDL <100, 78.7% with TG <150, 72.7% HTN <130/80, 90.1% non /former smoker, 30.9% of patients with BMI less than 30, 100% with HbA1c <7 in SLE patients, and 59.4% with LDL <100, 78.1% TG, 59.4% HTN <130/80, 75.0% non /former smoker, 21.9% with BMI less than 30, 61.3% with HbA1c <7 in RA patients. Of the total 31 medical residents completed the survey for the knowledge evaluation in CV risk management. Approximately 25.80% acknowledged all rheumatological diseases associated with increased risk for CVD, 58.06% identified appropriate CV risk factors, 25.8% can identify all appropriate orders for patients at risk, and 58.84% expressed lack of experience in working directly with patients with rheumatological conditions. Conclusions This study showed a strong prevalence for traditional risk factors among rheumatic patients with inadequate control. We also found an education gap in medical training regarding CV screening and management in patients with rheumatological diseases, namely SLE and RA in this study. For future studies, further investigation into improving knowledge in medical residency training as well as patient awareness should be investigated.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.240
Teacher spread0.234 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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