Features associated with cardiac abnormalities in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of echocardiographic abnormalities and identify associated clinical and laboratory features in a large systemic lupus erythematosus (SLE) cohort. METHODS: Patients fulfilling ACR criteria for SLE underwent a transthoracic echocardiogram (TTE) between January 2005 and June 2006. Variables used as potential correlates included age, sex, ethnicity, lupus duration, lupus disease activity (SLEDAI), cumulative damage (SLICC/ACR damage index (DI)), arterial hypertension, diabetes, current smoking, medication use and laboratory data. Multivariate logistic regression was used to examine the association between TTE abnormalities and potential determinants. RESULTS: For the 217 subjects with a TTE performed during the study, the main abnormalities were of the mitral valve (37.3%) and included thickening (25.4%) and insufficiency (25.8%). Other findings included pulmonary artery pressure (PAP) ≥ 30( )mm( )Hg (10.1%), pericardial effusion (4.6%), hypokinesis (2.8%), and aortic insufficiency (3.7%). In multivariate analysis, mitral insufficiency was associated with the use of corticosteroids (OR 2.90; 95%CI 1.42-5.94) and hypokinesis with angiotensin-converting enzyme inhibitors (12.89; 1.06-157.18). Elevated PAP was associated with age (1.04; 1.01-1.07) and with DI (1.20; 1.01-1.42). CONCLUSION: Valvular abnormalities are frequent in patients with SLE, with mitral valve lesions occurring in over one third. TTE screening may be indicated in patients with SLE, especially for those with identified risk factors such as corticosteroid use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".