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Record W2091096074 · doi:10.3899/jrheum.090312

Pattern of Arterial Calcification in Patients with Systemic Lupus Erythematosus

2009· article· en· W2091096074 on OpenAlexvenueno aff
Kai‐Hang Yiu, Silun Wang, Mo-Yin Mok, G.C. Ooi, Pek‐Lan Khong, Ka-Fung Henry Mak, Kwok–Fai Lam, Hung‐Fat Tse

Bibliographic record

VenueThe Journal of Rheumatology · 2009
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineAsymptomaticCardiologyCalcificationAgatston scoreAortaArteryCalcinosisCoronary artery diseaseCoronary artery calcium

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the prevalence and pattern of arterial calcification in patients with asymptomatic systemic lupus erythematosus (SLE) compared with control subjects. SLE patients are prone to adverse cardiovascular events; however, the underlying atherosclerotic process is unknown. Multidetector computed tomography (MDCT) measured arterial calcium score (CS) reflecting underlying atherosclerosis and is closely associated with cardiovascular events. METHODS: Fifty age and sex matched SLE patients and controls were enrolled. All subjects underwent 64 slice MDCT scan to evaluate CS in coronary, carotid arteries and the aorta. RESULTS: As compared with controls, SLE patients had higher mean CS and prevalence of CS > 0 across all vascular beds. After adjustment for age and sex, SLE patient odds of having CS > 0 in any vascular bed was 33.6 (95% CI: 9.5-165.2) were higher versus patients in the control group, mainly due to more prevalent coronary calcification (OR 30.0, 95% CI: 6.7-203.8). In SLE patients, the most frequent vessel with CS > 0 was coronary (42%) followed by carotid artery (24%). Further, arterial calcification occurred early involving 40% of SLE patients at age < 40 years, with increasing prevalence as age advanced. CONCLUSION: Our study confirms that patients with SLE have significantly higher prevalence and extent of systemic arterial calcification compared with age and sex matched controls.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.013
GPT teacher head0.265
Teacher spread0.252 · 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 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

Citations56
Published2009
Admission routes1
Has abstractyes

Explore more

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