Bibliographic record
Abstract
Whether atherosclerosis is accelerated in patients with systemic lupus erythematosus (SLE) is not a matter of debate any more. Since Urowitz, et al’ s first study1, myocardial infarction (MI) has been recognized as one of the leading causes of death in patients with SLE, particularly in those with long-lasting disease. The frequency of MI as a cause of death in SLE patients has also proportionately increased in the last few decades due to the decrease in deaths directly related to SLE, a consequence of improvement in SLE treatments and the longer survival of patients2. Interestingly, in post mortem studies, significant atherosclerosis was observed in more than 50% of patients regardless of the cause of death3. In addition, clinical and epidemiologic studies have thoroughly documented the higher prevalence of coronary artery disease (CAD) in patients with SLE compared with the general population, which ranges between 6% and 10%4. These findings were expanded in studies using diagnostic methodologies such as scintigraphy with thallium-201, single photon-emission computed tomography dual-isotope myocardial perfusion imaging, electron-beam computed tomography, and carotid B-mode ultrasound (US): prevalence of subclinical atherosclerosis was shown to be even higher than that of clinical atherosclerosis, with values ranging between 10% and 40%4. It is still unknown, however, whether cardiac perfusion abnormalities, carotid plaques, or calcifications in coronary arteries in patients with SLE are as predictive of cardiovascular (CV) events as in the general population. Studies using either clinical or subclinical outcomes clearly showed that excess atherosclerosis in SLE patients cannot be attributed to “classic” Framingham risk factors, but derives from a complex interaction between traditional and nontraditional predictors5. Notably, in SLE a great variability in the predictors … Address correspondence to Prof. Doria; E-mail adoria{at}unipd.it
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.023 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".