Single photon emission computed tomography dual isotope myocardial perfusion imaging in women with systemic lupus erythematosus. I. Prevalence and distribution of abnormalities.
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of myocardial perfusion abnormalities in women with systemic lupus erythematosus (SLE) using single photon emission computed tomography (SPECT) dual isotope myocardial perfusion imaging (DIMPI). METHODS: Consecutive female patients registered at the University of Toronto Lupus Clinic were offered DIMPI evaluation and all who accepted were studied. Patients underwent SPECT DIMPI using dipyridamole stress. Resting and stress images were acquired using thallium-201 (201TI) and technetium 99m-sestamibi (99mTc sestamibi), respectively. We recorded segmental perfusion abnormalities, severity and reversibility of any abnormality, and number of vessel territories involved. Ejection fraction was also measured. RESULTS: One hundred thirty patients were studied. Mean (SD) age and disease duration at study were 45.1 (11.1) years and 14.6 (9.4) years, respectively. Thirteen patients (10%) had a history of angina pectoris or myocardial infarction. Overall, 52 (40%) patients had an abnormality of myocardial perfusion, including 11 (85%) with a history of angina or myocardial infarction. In those with no history of coronary artery disease, 41 (35%) had an abnormality detected. The perfusion defect was reversible in 47 (90%). In 37 (71%) cases perfusion defects were seen in the region of a single vessel territory. Eighteen (13.8%) patients had an ejection fraction (EF) < 50%. CONCLUSION: Using SPECT DIMPI, 40% of all women with SLE and 35% of women with SLE with no history of coronary artery disease had abnormalities of myocardial perfusion, suggesting a high prevalence of early coronary artery disease. The early detection of disease will facilitate study of atherosclerotic risk factors; such women can also be targeted for a focused program of risk factor management.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".