Important issues at heart: cardiovascular risk management in rheumatoid arthritis
Bibliographic record
Abstract
Atherosclerosis remains the predominant cause of cardiovascular diseases (CVDs) and heart failure, which are the leading causes of death worldwide. It is now well established that patients with rheumatoid arthritis (RA) experience an accelerated atherosclerosis increasing their risk of CVD and related mortality compared with the general population [Nicola et al. 2005; Roubille and Tardif, 2013; Solomon et al. 2003]: a recent meta-analysis highlighted an increased risk of all CVDs of 48% in RA, as well as of myocardial infarction of 68% and of strokes of 41% [Avina-Zubieta et al. 2012]. Over the past decade, inflammation appeared to be the cornerstone of both atherosclerosis and RA. Indeed, atherosclerosis is no longer considered a condition in which lipids are being deposited passively in the arterial wall but rather fully recognized as an inflammatory, dynamic and complex disease involving multiple cell types, including inflammatory cells as well as endothelial and smooth muscle cells [Newby, 2010; Roubille et al. 2013c; Shah, 2009]. RA has long been recognized as a systemic inflammatory syndrome with several extra-articular manifestations such as interstitial lung disease and vasculitis. However, these features do not occur in all RA patients. In contrast, vascular inflammation and accelerated atherosclerosis may affect a larger RA population, probably all RA patients, especially in the early stage of the disease, as pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-alpha), interleukin (IL)-1 and IL-6 as well as B cells contribute to the pathogenesis of both atherosclerosis and RA [Hansson, 2005]. Moreover, traditional cardiovascular risk factors should not be overlooked. Hence, assessment and management of cardiovascular risk factors is recommended for RA patients [Haraoui et al. 2012; Peters et al. 2010]. The treatment goal should be to achieve remission or at least low disease activity as early as possible [Smolen et al. 2010], not only to lead to better structural and functional outcomes, but also to reduce the cardiovascular risk [Peters et al. 2010].
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".