Statins and Mortality in Connective Tissue Diseases: Should We Resume the Cardio-rheumatology Spirit in Our Clinics?
Bibliographic record
Abstract
In 1912, Windaus reported that atherosclerotic plaques from aortas of human subjects contained over 30-fold higher concentrations of cholesterol than did normal aortas1, and in 1913 the Russian pathologist Nikolai Anitschkow2, feeding pure cholesterol to rabbits, produced marked hypercholesterolemia and severe atherosclerosis of the aorta. In 1974, Brown and Goldstein discovered the low-density lipoprotein (LDL) receptor and the regulation of cholesterol metabolism in the cells3, and in 1976 Akira Endo4 discovered a fungal metabolite that could block cholesterol synthesis by inhibiting the enzyme hydroxymethylglutaryl CoA. These key steps paved the way to the understanding of LDL cholesterol (LDL-C) levels as one of the primary targets in prevention of ischemic heart attacks and to the discovery of statins as the therapeutic drug capable of reducing the cardiovascular (CV) risk. Since they were first approved in 1987, statins have represented substantial potential for safe, effective, and inexpensive primary prevention of ASCVD (atherosclerotic CV diseases). Among the several risk factors defined in the last 20 years, inflammation and inflammatory biomarkers such as high-sensitivity C-reactive protein (hsCRP) and interleukin 6 have arisen as predictors of future CV events, along with conventional LDL-C or high-density lipoprotein cholesterol (HDL-C). Randomized trial data have also shown that statins reduce not only hsCRP but also CV event rates independently of their effect on LDL-C level. This led to a focus on a further effect of statins: their antiinflammatory effect5,6,7, which appears particularly important in all rheumatic diseases [the chronic arthritides as well … Address correspondence to Dr. G.F. Ferraccioli, Institute of Rheumatology, Università Cattolica del Sacro Cuore, Via Moscati 31, 00168 Rome, Italy. E-mail: gianfranco.ferraccioli{at}unicatt.it
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 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".