Cardiovascular Risk in Rheumatoid Arthritis (RA): Does It Matter If RA Is Diagnosed in Early or Late Age?
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a complex autoimmune disorder with heterogeneous clinical presentations, including age at disease onset. Indeed, RA can be diagnosed at any age. Although the peak age of onset is between 50 and 75 years, the prevalence of RA in females over age 65 is up to 5%1. Given the prevalence of RA is about 1% in whites2, there is a substantial subgroup of RA patients who are elderly (over age 65) at disease onset. There are over 50 countries with current life expectancies of 75 years or older3. Hence, the risk of morbidity and mortality even in the subgroup of elderly-onset RA patients is of clinical relevance. The approximately 2 to 3-fold increased risk of cardiovascular disease (CVD) in RA is well-established4,5,6. It is suggested that both traditional CV risk factors as well as RA-specific disease mechanisms (e.g., systemic inflammation, immune dysregulation, and premature immunosenescence) contribute to the overall CV risk in RA. However, few studies have examined the risks and determinants of CVD in subgroups of RA patients depending on their age at diagnosis. In a previous population-based study6, it was shown that the 10-year absolute cardiovascular (CV) risk in given age groups varied substantially according to the presence of CV risk factors; among 60 to 69-year-old RA patients with no risk factors, the absolute CV risk was only 16.8%, but rose to 60.4% if CV risk factors of smoking, hypertension (HTN), dyslipidemia, diabetes, and obesity were present. A recent study by Ajeganova and colleagues in this issue of The Journal 7 complements these findings by examining the effect of early RA disease factors on CV risk, stratified by age at RA disease onset. Their study sought to examine the associations of … Address correspondence to Dr. Liang, University of Pittsburgh, Division of Rheumatology, 3500 Terrace St., BST S723, Pittsburgh, PA 15261, USA. E-mail: liangkp{at}upmc.edu
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".