Single photon emission computed tomography dual isotope myocardial perfusion imaging in women with systemic lupus erythematosus. II. Predictive factors for perfusion abnormalities.
Bibliographic record
Abstract
OBJECTIVE: We have reported that 40% of patients with systemic lupus erythematosus (SLE) had abnormal myocardial perfusion studies. Here we investigated risk factors for abnormal myocardial perfusion in a cohort of women with SLE without history of coronary artery disease. METHODS: Consecutive women with SLE followed at a large lupus clinic underwent single photon emission computed tomography dual isotope myocardial perfusion imaging (DIMPI) following pharmacological stress using dipyridamole. At the time of study each patient had a clinical and laboratory assessment performed by a standard protocol. We compared traditional risk factors as well as disease and therapy related factors in those with and without perfusion abnormalities. RESULTS: A total of 129 patients were studied. The mean +/- SD age was 44.8 +/- 10.9 yrs, and mean SLE Disease Activity Index was 4.2 +/- 5.1. Forty-nine (38%) patients had an abnormality of myocardial perfusion. Factors associated with an abnormal DIMPI included current hypertension (OR 2.11, p = 0.05), elevated cholesterol ever (OR 2.51, p < 0.05), and total cholesterol:high density lipoprotein-cholesterol ratio (OR 1.96 for each increase of 1.0, p < 0.008). CONCLUSION: Myocardial perfusion abnormalities are common in women with SLE without known coronary artery disease (CAD), suggesting a high burden of subclinical CAD. Several metabolic and therapy related factors appear to be associated with the process of atherogenesis in SLE. These results suggest that SLE should be considered a predisposing factor for atherosclerosis.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".