Vascular Imaging as a Cardiovascular Risk Stratification Tool in Systemic Lupus Erythematosus
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is an autoimmune disorder associated with increased incidence of cardiovascular disease (CVD) and consequently, a higher rate of cardiovascular events1. This association has been mainly established on epidemiological data and, given that patients with SLE are predominantly premenopausal women, this excess in CVD risk might appear somewhat unexpected1,2. Fifty years ago, only 50% of patients with SLE survived 5 years after diagnosis; but today, with better therapeutic strategies, the disease outcome is better, and 80% to 90% of patients survive at least 10 years after SLE is diagnosed3. With the increased life expectancy of patients with SLE, CVD has perhaps emerged as a more significant threat to their health. For a long time, the effect of this problem has been under-recognized, with little focus on aggressive management of CVD risk factors or the development of risk stratification strategies. The high risk for CVD in patients with SLE was substantiated by studies that assessed the incidence of major cardiovascular events and studies that noninvasively evaluated the atherosclerotic burden in SLE. Almost 3 decades ago, Urowitz, et al followed up 81 SLE patients for 5 years and reported that in the majority of those who died late in the course of the disease, myocardial infarction was the primary cause of death3. These authors first described a phenomenon that is still known as the “bimodal mortality pattern” of SLE. Recent prospective studies have also confirmed this unusual bimodal pattern of mortality in SLE. Manzi, et al 4 reported that women with SLE at the age of 35 to 44 years were over 50 times more likely to have a myocardial infarction than women of similar age in the Framingham Offspring Study. The authors concluded that CVD was much more … Address correspondence to Prof. Lip. E-mail:g.y.h.lip{at}bham.ac.uk
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".